Accounting for Global COVID-19 Diffusion Patterns, January-April 2020

authors: “Sameer Nair-Desai, Rashad Ahmed, Weining Xin” date: “5/22/2020” output: html_document: default pdf_document: default word_document: default —

If you do not wish to run the code, please see the final output (graphs and datasets) provided in the Github repository. Make sure to check the relevant README files for the contents of the folder.

Installing the relevant packages. install.packages(“sf”) install.packages(“readr”) install.packages(“tmap”) install.packages(“leaflet”) install.packages(“mapview”) install.packages(“ggplot2”) install.packages(“tidyverse”) install.packages(“rlang”) install.packages(“reshape”) install.packages(“rgdal”) install.packages(“lubridate”) install.packages(“plotly”) install.packages(“patchwork”) install.packages(“ggforce”) install.packages(“gridExtra”) install.packages(“htmltools”) install.packages(“data.table”) install.packages(“webshot”) webshot::install_phantomjs() install.packages(“runner”) install.packages(“zoo”) install.packages(“devtools”) devtools::install_version(“latticeExtra”, version=“0.6-28”) install.packages(“Hmisc”) install.packages(“DataCombine”) install.packages(“fastDummies”) install.packages(“heatmaply”) install.packages(“glmnet”) install.packages(“caret”) install.packages(“summarytools”) install.packages(“remote”) remotes::install_github(‘rapporter/pander’) install.packages(“mlbench”) install.packages(“psych”) install.packages(“lmtest”) install.packages(“quantmod”) install.packages(“corrplot”) install.packages(“fBasics”) install.packages(“stargazer”) install.packages(“tseries”) install.packages(“vars”) install.packages(“gghighlight”)

# Setting our initial libraries. In subsequent rounds of analysis, we will be integrating new libraries (we do not do so here to avoid conflicts between packages).
library(sf)

## Warning: package 'sf' was built under R version 3.6.2

## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0

library(readr)
library(mapview)

## Warning: package 'mapview' was built under R version 3.6.2

library(ggplot2)
library(tidyverse)

## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ tibble  3.0.1     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.3     ✓ stringr 1.4.0
## ✓ purrr   0.3.4     ✓ forcats 0.5.0

## Warning: package 'tibble' was built under R version 3.6.2

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## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(rlang)

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library(reshape)

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library(rgdal)

## Warning: package 'rgdal' was built under R version 3.6.2

## Loading required package: sp

## rgdal: version: 1.5-8, (SVN revision 990)
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## Loaded GDAL runtime: GDAL 2.4.2, released 2019/06/28
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## GDAL binary built with GEOS: FALSE 
## Loaded PROJ runtime: Rel. 5.2.0, September 15th, 2018, [PJ_VERSION: 520]
## Path to PROJ shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/proj
## Linking to sp version:1.4-2
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.

library(lubridate)

## Warning: package 'lubridate' was built under R version 3.6.2

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library(plotly)

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library(patchwork)
library(ggforce)
library(gridExtra)

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library(htmltools)
library(data.table)

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library(webshot)
library(coronavirus)

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library(runner)

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library(zoo)

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library(DataCombine)

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library(fastDummies)
library(car)

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library(heatmaply)

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## 
## ======================
## Welcome to heatmaply version 1.1.0
## 
## Type citation('heatmaply') for how to cite the package.
## Type ?heatmaply for the main documentation.
## 
## The github page is: https://github.com/talgalili/heatmaply/
## Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
## Or contact: <tal.galili@gmail.com>
## ======================

library(htmlwidgets)
library(summarytools)

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library(glmnet)

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library(caret)

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library(mlbench)
library(psych)

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library(plm)

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library(lmtest)
library(quantmod)

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library(leaflet)

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library(corrplot)

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library(fBasics)

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library(stargazer)

## 
## Please cite as:

##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer

library(tseries)
library(vars)

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library(dplyr)
library(gghighlight)

## Warning: package 'gghighlight' was built under R version 3.6.2

# I don't want scientific notation for my values, so I specify this below.
options(scipen = 999)

CLEANING - Note that initial confirmed cases and deaths are recorded as cumulative sums in the Hopkins dataset. We will generate new (daily and weekly) case and death data.

# Import and quickly cleaning our data. We will call the import our initial confirmed data (Wide). 
Initial_Confirmed_Wide <- read_csv("data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   `Province/State` = col_character(),
##   `Country/Region` = col_character()
## )

## See spec(...) for full column specifications.

Creating our Temporary and World data files.

# Our data is in wide format. Here, I translate it to long format.
Confirmed_Long <- pivot_longer(Initial_Confirmed_Wide, cols = c(5:102), names_to = "Dates", values_to = "Confirmed_Cases")

# Now, I rename my column for country in the long dataset.
Confirmed_Long$COUNTRY <- Confirmed_Long$`Country/Region`

# I can also convert the dates into a more usable string format with lubridate.
Confirmed_Long$Date <- mdy(Confirmed_Long$Dates, quiet = FALSE, tz = NULL, locale = Sys.getlocale("LC_TIME"),
  truncated = 0)
order(Confirmed_Long$Date)

##     [1]     1    99   197   295   393   491   589   687   785   883   981  1079
##    [13]  1177  1275  1373  1471  1569  1667  1765  1863  1961  2059  2157  2255
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##   [625]  9411  9509  9607  9705  9803  9901  9999 10097 10195 10293 10391 10489
##   [637] 10587 10685 10783 10881 10979 11077 11175 11273 11371 11469 11567 11665
##   [649] 11763 11861 11959 12057 12155 12253 12351 12449 12547 12645 12743 12841
##   [661] 12939 13037 13135 13233 13331 13429 13527 13625 13723 13821 13919 14017
##   [673] 14115 14213 14311 14409 14507 14605 14703 14801 14899 14997 15095 15193
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##   [781] 24699 24797 24895 24993 25091 25189 25287 25385 25483 25581 25679 25777
##   [793]     4   102   200   298   396   494   592   690   788   886   984  1082
##   [805]  1180  1278  1376  1474  1572  1670  1768  1866  1964  2062  2160  2258
##   [817]  2356  2454  2552  2650  2748  2846  2944  3042  3140  3238  3336  3434
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##   [841]  4708  4806  4904  5002  5100  5198  5296  5394  5492  5590  5688  5786
##   [853]  5884  5982  6080  6178  6276  6374  6472  6570  6668  6766  6864  6962
##   [865]  7060  7158  7256  7354  7452  7550  7648  7746  7844  7942  8040  8138
##   [877]  8236  8334  8432  8530  8628  8726  8824  8922  9020  9118  9216  9314
##   [889]  9412  9510  9608  9706  9804  9902 10000 10098 10196 10294 10392 10490
##   [901] 10588 10686 10784 10882 10980 11078 11176 11274 11372 11470 11568 11666
##   [913] 11764 11862 11960 12058 12156 12254 12352 12450 12548 12646 12744 12842
##   [925] 12940 13038 13136 13234 13332 13430 13528 13626 13724 13822 13920 14018
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##  [1057]     5   103   201   299   397   495   593   691   789   887   985  1083
##  [1069]  1181  1279  1377  1475  1573  1671  1769  1867  1965  2063  2161  2259
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##  [1321]     6   104   202   300   398   496   594   692   790   888   986  1084
##  [1333]  1182  1280  1378  1476  1574  1672  1770  1868  1966  2064  2162  2260
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##  [1369]  4710  4808  4906  5004  5102  5200  5298  5396  5494  5592  5690  5788
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##  [1393]  7062  7160  7258  7356  7454  7552  7650  7748  7846  7944  8042  8140
##  [1405]  8238  8336  8434  8532  8630  8728  8826  8924  9022  9120  9218  9316
##  [1417]  9414  9512  9610  9708  9806  9904 10002 10100 10198 10296 10394 10492
##  [1429] 10590 10688 10786 10884 10982 11080 11178 11276 11374 11472 11570 11668
##  [1441] 11766 11864 11962 12060 12158 12256 12354 12452 12550 12648 12746 12844
##  [1453] 12942 13040 13138 13236 13334 13432 13530 13628 13726 13824 13922 14020
##  [1465] 14118 14216 14314 14412 14510 14608 14706 14804 14902 15000 15098 15196
##  [1477] 15294 15392 15490 15588 15686 15784 15882 15980 16078 16176 16274 16372
##  [1489] 16470 16568 16666 16764 16862 16960 17058 17156 17254 17352 17450 17548
##  [1501] 17646 17744 17842 17940 18038 18136 18234 18332 18430 18528 18626 18724
##  [1513] 18822 18920 19018 19116 19214 19312 19410 19508 19606 19704 19802 19900
##  [1525] 19998 20096 20194 20292 20390 20488 20586 20684 20782 20880 20978 21076
##  [1537] 21174 21272 21370 21468 21566 21664 21762 21860 21958 22056 22154 22252
##  [1549] 22350 22448 22546 22644 22742 22840 22938 23036 23134 23232 23330 23428
##  [1561] 23526 23624 23722 23820 23918 24016 24114 24212 24310 24408 24506 24604
##  [1573] 24702 24800 24898 24996 25094 25192 25290 25388 25486 25584 25682 25780
##  [1585]     7   105   203   301   399   497   595   693   791   889   987  1085
##  [1597]  1183  1281  1379  1477  1575  1673  1771  1869  1967  2065  2163  2261
##  [1609]  2359  2457  2555  2653  2751  2849  2947  3045  3143  3241  3339  3437
##  [1621]  3535  3633  3731  3829  3927  4025  4123  4221  4319  4417  4515  4613
##  [1633]  4711  4809  4907  5005  5103  5201  5299  5397  5495  5593  5691  5789
##  [1645]  5887  5985  6083  6181  6279  6377  6475  6573  6671  6769  6867  6965
##  [1657]  7063  7161  7259  7357  7455  7553  7651  7749  7847  7945  8043  8141
##  [1669]  8239  8337  8435  8533  8631  8729  8827  8925  9023  9121  9219  9317
##  [1681]  9415  9513  9611  9709  9807  9905 10003 10101 10199 10297 10395 10493
##  [1693] 10591 10689 10787 10885 10983 11081 11179 11277 11375 11473 11571 11669
##  [1705] 11767 11865 11963 12061 12159 12257 12355 12453 12551 12649 12747 12845
##  [1717] 12943 13041 13139 13237 13335 13433 13531 13629 13727 13825 13923 14021
##  [1729] 14119 14217 14315 14413 14511 14609 14707 14805 14903 15001 15099 15197
##  [1741] 15295 15393 15491 15589 15687 15785 15883 15981 16079 16177 16275 16373
##  [1753] 16471 16569 16667 16765 16863 16961 17059 17157 17255 17353 17451 17549
##  [1765] 17647 17745 17843 17941 18039 18137 18235 18333 18431 18529 18627 18725
##  [1777] 18823 18921 19019 19117 19215 19313 19411 19509 19607 19705 19803 19901
##  [1789] 19999 20097 20195 20293 20391 20489 20587 20685 20783 20881 20979 21077
##  [1801] 21175 21273 21371 21469 21567 21665 21763 21861 21959 22057 22155 22253
##  [1813] 22351 22449 22547 22645 22743 22841 22939 23037 23135 23233 23331 23429
##  [1825] 23527 23625 23723 23821 23919 24017 24115 24213 24311 24409 24507 24605
##  [1837] 24703 24801 24899 24997 25095 25193 25291 25389 25487 25585 25683 25781
##  [1849]     8   106   204   302   400   498   596   694   792   890   988  1086
##  [1861]  1184  1282  1380  1478  1576  1674  1772  1870  1968  2066  2164  2262
##  [1873]  2360  2458  2556  2654  2752  2850  2948  3046  3144  3242  3340  3438
##  [1885]  3536  3634  3732  3830  3928  4026  4124  4222  4320  4418  4516  4614
##  [1897]  4712  4810  4908  5006  5104  5202  5300  5398  5496  5594  5692  5790
##  [1909]  5888  5986  6084  6182  6280  6378  6476  6574  6672  6770  6868  6966
##  [1921]  7064  7162  7260  7358  7456  7554  7652  7750  7848  7946  8044  8142
##  [1933]  8240  8338  8436  8534  8632  8730  8828  8926  9024  9122  9220  9318
##  [1945]  9416  9514  9612  9710  9808  9906 10004 10102 10200 10298 10396 10494
##  [1957] 10592 10690 10788 10886 10984 11082 11180 11278 11376 11474 11572 11670
##  [1969] 11768 11866 11964 12062 12160 12258 12356 12454 12552 12650 12748 12846
##  [1981] 12944 13042 13140 13238 13336 13434 13532 13630 13728 13826 13924 14022
##  [1993] 14120 14218 14316 14414 14512 14610 14708 14806 14904 15002 15100 15198
##  [2005] 15296 15394 15492 15590 15688 15786 15884 15982 16080 16178 16276 16374
##  [2017] 16472 16570 16668 16766 16864 16962 17060 17158 17256 17354 17452 17550
##  [2029] 17648 17746 17844 17942 18040 18138 18236 18334 18432 18530 18628 18726
##  [2041] 18824 18922 19020 19118 19216 19314 19412 19510 19608 19706 19804 19902
##  [2053] 20000 20098 20196 20294 20392 20490 20588 20686 20784 20882 20980 21078
##  [2065] 21176 21274 21372 21470 21568 21666 21764 21862 21960 22058 22156 22254
##  [2077] 22352 22450 22548 22646 22744 22842 22940 23038 23136 23234 23332 23430
##  [2089] 23528 23626 23724 23822 23920 24018 24116 24214 24312 24410 24508 24606
##  [2101] 24704 24802 24900 24998 25096 25194 25292 25390 25488 25586 25684 25782
##  [2113]     9   107   205   303   401   499   597   695   793   891   989  1087
##  [2125]  1185  1283  1381  1479  1577  1675  1773  1871  1969  2067  2165  2263
##  [2137]  2361  2459  2557  2655  2753  2851  2949  3047  3145  3243  3341  3439
##  [2149]  3537  3635  3733  3831  3929  4027  4125  4223  4321  4419  4517  4615
##  [2161]  4713  4811  4909  5007  5105  5203  5301  5399  5497  5595  5693  5791
##  [2173]  5889  5987  6085  6183  6281  6379  6477  6575  6673  6771  6869  6967
##  [2185]  7065  7163  7261  7359  7457  7555  7653  7751  7849  7947  8045  8143
##  [2197]  8241  8339  8437  8535  8633  8731  8829  8927  9025  9123  9221  9319
##  [2209]  9417  9515  9613  9711  9809  9907 10005 10103 10201 10299 10397 10495
##  [2221] 10593 10691 10789 10887 10985 11083 11181 11279 11377 11475 11573 11671
##  [2233] 11769 11867 11965 12063 12161 12259 12357 12455 12553 12651 12749 12847
##  [2245] 12945 13043 13141 13239 13337 13435 13533 13631 13729 13827 13925 14023
##  [2257] 14121 14219 14317 14415 14513 14611 14709 14807 14905 15003 15101 15199
##  [2269] 15297 15395 15493 15591 15689 15787 15885 15983 16081 16179 16277 16375
##  [2281] 16473 16571 16669 16767 16865 16963 17061 17159 17257 17355 17453 17551
##  [2293] 17649 17747 17845 17943 18041 18139 18237 18335 18433 18531 18629 18727
##  [2305] 18825 18923 19021 19119 19217 19315 19413 19511 19609 19707 19805 19903
##  [2317] 20001 20099 20197 20295 20393 20491 20589 20687 20785 20883 20981 21079
##  [2329] 21177 21275 21373 21471 21569 21667 21765 21863 21961 22059 22157 22255
##  [2341] 22353 22451 22549 22647 22745 22843 22941 23039 23137 23235 23333 23431
##  [2353] 23529 23627 23725 23823 23921 24019 24117 24215 24313 24411 24509 24607
##  [2365] 24705 24803 24901 24999 25097 25195 25293 25391 25489 25587 25685 25783
##  [2377]    10   108   206   304   402   500   598   696   794   892   990  1088
##  [2389]  1186  1284  1382  1480  1578  1676  1774  1872  1970  2068  2166  2264
##  [2401]  2362  2460  2558  2656  2754  2852  2950  3048  3146  3244  3342  3440
##  [2413]  3538  3636  3734  3832  3930  4028  4126  4224  4322  4420  4518  4616
##  [2425]  4714  4812  4910  5008  5106  5204  5302  5400  5498  5596  5694  5792
##  [2437]  5890  5988  6086  6184  6282  6380  6478  6576  6674  6772  6870  6968
##  [2449]  7066  7164  7262  7360  7458  7556  7654  7752  7850  7948  8046  8144
##  [2461]  8242  8340  8438  8536  8634  8732  8830  8928  9026  9124  9222  9320
##  [2473]  9418  9516  9614  9712  9810  9908 10006 10104 10202 10300 10398 10496
##  [2485] 10594 10692 10790 10888 10986 11084 11182 11280 11378 11476 11574 11672
##  [2497] 11770 11868 11966 12064 12162 12260 12358 12456 12554 12652 12750 12848
##  [2509] 12946 13044 13142 13240 13338 13436 13534 13632 13730 13828 13926 14024
##  [2521] 14122 14220 14318 14416 14514 14612 14710 14808 14906 15004 15102 15200
##  [2533] 15298 15396 15494 15592 15690 15788 15886 15984 16082 16180 16278 16376
##  [2545] 16474 16572 16670 16768 16866 16964 17062 17160 17258 17356 17454 17552
##  [2557] 17650 17748 17846 17944 18042 18140 18238 18336 18434 18532 18630 18728
##  [2569] 18826 18924 19022 19120 19218 19316 19414 19512 19610 19708 19806 19904
##  [2581] 20002 20100 20198 20296 20394 20492 20590 20688 20786 20884 20982 21080
##  [2593] 21178 21276 21374 21472 21570 21668 21766 21864 21962 22060 22158 22256
##  [2605] 22354 22452 22550 22648 22746 22844 22942 23040 23138 23236 23334 23432
##  [2617] 23530 23628 23726 23824 23922 24020 24118 24216 24314 24412 24510 24608
##  [2629] 24706 24804 24902 25000 25098 25196 25294 25392 25490 25588 25686 25784
##  [2641]    11   109   207   305   403   501   599   697   795   893   991  1089
##  [2653]  1187  1285  1383  1481  1579  1677  1775  1873  1971  2069  2167  2265
##  [2665]  2363  2461  2559  2657  2755  2853  2951  3049  3147  3245  3343  3441
##  [2677]  3539  3637  3735  3833  3931  4029  4127  4225  4323  4421  4519  4617
##  [2689]  4715  4813  4911  5009  5107  5205  5303  5401  5499  5597  5695  5793
##  [2701]  5891  5989  6087  6185  6283  6381  6479  6577  6675  6773  6871  6969
##  [2713]  7067  7165  7263  7361  7459  7557  7655  7753  7851  7949  8047  8145
##  [2725]  8243  8341  8439  8537  8635  8733  8831  8929  9027  9125  9223  9321
##  [2737]  9419  9517  9615  9713  9811  9909 10007 10105 10203 10301 10399 10497
##  [2749] 10595 10693 10791 10889 10987 11085 11183 11281 11379 11477 11575 11673
##  [2761] 11771 11869 11967 12065 12163 12261 12359 12457 12555 12653 12751 12849
##  [2773] 12947 13045 13143 13241 13339 13437 13535 13633 13731 13829 13927 14025
##  [2785] 14123 14221 14319 14417 14515 14613 14711 14809 14907 15005 15103 15201
##  [2797] 15299 15397 15495 15593 15691 15789 15887 15985 16083 16181 16279 16377
##  [2809] 16475 16573 16671 16769 16867 16965 17063 17161 17259 17357 17455 17553
##  [2821] 17651 17749 17847 17945 18043 18141 18239 18337 18435 18533 18631 18729
##  [2833] 18827 18925 19023 19121 19219 19317 19415 19513 19611 19709 19807 19905
##  [2845] 20003 20101 20199 20297 20395 20493 20591 20689 20787 20885 20983 21081
##  [2857] 21179 21277 21375 21473 21571 21669 21767 21865 21963 22061 22159 22257
##  [2869] 22355 22453 22551 22649 22747 22845 22943 23041 23139 23237 23335 23433
##  [2881] 23531 23629 23727 23825 23923 24021 24119 24217 24315 24413 24511 24609
##  [2893] 24707 24805 24903 25001 25099 25197 25295 25393 25491 25589 25687 25785
##  [2905]    12   110   208   306   404   502   600   698   796   894   992  1090
##  [2917]  1188  1286  1384  1482  1580  1678  1776  1874  1972  2070  2168  2266
##  [2929]  2364  2462  2560  2658  2756  2854  2952  3050  3148  3246  3344  3442
##  [2941]  3540  3638  3736  3834  3932  4030  4128  4226  4324  4422  4520  4618
##  [2953]  4716  4814  4912  5010  5108  5206  5304  5402  5500  5598  5696  5794
##  [2965]  5892  5990  6088  6186  6284  6382  6480  6578  6676  6774  6872  6970
##  [2977]  7068  7166  7264  7362  7460  7558  7656  7754  7852  7950  8048  8146
##  [2989]  8244  8342  8440  8538  8636  8734  8832  8930  9028  9126  9224  9322
##  [3001]  9420  9518  9616  9714  9812  9910 10008 10106 10204 10302 10400 10498
##  [3013] 10596 10694 10792 10890 10988 11086 11184 11282 11380 11478 11576 11674
##  [3025] 11772 11870 11968 12066 12164 12262 12360 12458 12556 12654 12752 12850
##  [3037] 12948 13046 13144 13242 13340 13438 13536 13634 13732 13830 13928 14026
##  [3049] 14124 14222 14320 14418 14516 14614 14712 14810 14908 15006 15104 15202
##  [3061] 15300 15398 15496 15594 15692 15790 15888 15986 16084 16182 16280 16378
##  [3073] 16476 16574 16672 16770 16868 16966 17064 17162 17260 17358 17456 17554
##  [3085] 17652 17750 17848 17946 18044 18142 18240 18338 18436 18534 18632 18730
##  [3097] 18828 18926 19024 19122 19220 19318 19416 19514 19612 19710 19808 19906
##  [3109] 20004 20102 20200 20298 20396 20494 20592 20690 20788 20886 20984 21082
##  [3121] 21180 21278 21376 21474 21572 21670 21768 21866 21964 22062 22160 22258
##  [3133] 22356 22454 22552 22650 22748 22846 22944 23042 23140 23238 23336 23434
##  [3145] 23532 23630 23728 23826 23924 24022 24120 24218 24316 24414 24512 24610
##  [3157] 24708 24806 24904 25002 25100 25198 25296 25394 25492 25590 25688 25786
##  [3169]    13   111   209   307   405   503   601   699   797   895   993  1091
##  [3181]  1189  1287  1385  1483  1581  1679  1777  1875  1973  2071  2169  2267
##  [3193]  2365  2463  2561  2659  2757  2855  2953  3051  3149  3247  3345  3443
##  [3205]  3541  3639  3737  3835  3933  4031  4129  4227  4325  4423  4521  4619
##  [3217]  4717  4815  4913  5011  5109  5207  5305  5403  5501  5599  5697  5795
##  [3229]  5893  5991  6089  6187  6285  6383  6481  6579  6677  6775  6873  6971
##  [3241]  7069  7167  7265  7363  7461  7559  7657  7755  7853  7951  8049  8147
##  [3253]  8245  8343  8441  8539  8637  8735  8833  8931  9029  9127  9225  9323
##  [3265]  9421  9519  9617  9715  9813  9911 10009 10107 10205 10303 10401 10499
##  [3277] 10597 10695 10793 10891 10989 11087 11185 11283 11381 11479 11577 11675
##  [3289] 11773 11871 11969 12067 12165 12263 12361 12459 12557 12655 12753 12851
##  [3301] 12949 13047 13145 13243 13341 13439 13537 13635 13733 13831 13929 14027
##  [3313] 14125 14223 14321 14419 14517 14615 14713 14811 14909 15007 15105 15203
##  [3325] 15301 15399 15497 15595 15693 15791 15889 15987 16085 16183 16281 16379
##  [3337] 16477 16575 16673 16771 16869 16967 17065 17163 17261 17359 17457 17555
##  [3349] 17653 17751 17849 17947 18045 18143 18241 18339 18437 18535 18633 18731
##  [3361] 18829 18927 19025 19123 19221 19319 19417 19515 19613 19711 19809 19907
##  [3373] 20005 20103 20201 20299 20397 20495 20593 20691 20789 20887 20985 21083
##  [3385] 21181 21279 21377 21475 21573 21671 21769 21867 21965 22063 22161 22259
##  [3397] 22357 22455 22553 22651 22749 22847 22945 23043 23141 23239 23337 23435
##  [3409] 23533 23631 23729 23827 23925 24023 24121 24219 24317 24415 24513 24611
##  [3421] 24709 24807 24905 25003 25101 25199 25297 25395 25493 25591 25689 25787
##  [3433]    14   112   210   308   406   504   602   700   798   896   994  1092
##  [3445]  1190  1288  1386  1484  1582  1680  1778  1876  1974  2072  2170  2268
##  [3457]  2366  2464  2562  2660  2758  2856  2954  3052  3150  3248  3346  3444
##  [3469]  3542  3640  3738  3836  3934  4032  4130  4228  4326  4424  4522  4620
##  [3481]  4718  4816  4914  5012  5110  5208  5306  5404  5502  5600  5698  5796
##  [3493]  5894  5992  6090  6188  6286  6384  6482  6580  6678  6776  6874  6972
##  [3505]  7070  7168  7266  7364  7462  7560  7658  7756  7854  7952  8050  8148
##  [3517]  8246  8344  8442  8540  8638  8736  8834  8932  9030  9128  9226  9324
##  [3529]  9422  9520  9618  9716  9814  9912 10010 10108 10206 10304 10402 10500
##  [3541] 10598 10696 10794 10892 10990 11088 11186 11284 11382 11480 11578 11676
##  [3553] 11774 11872 11970 12068 12166 12264 12362 12460 12558 12656 12754 12852
##  [3565] 12950 13048 13146 13244 13342 13440 13538 13636 13734 13832 13930 14028
##  [3577] 14126 14224 14322 14420 14518 14616 14714 14812 14910 15008 15106 15204
##  [3589] 15302 15400 15498 15596 15694 15792 15890 15988 16086 16184 16282 16380
##  [3601] 16478 16576 16674 16772 16870 16968 17066 17164 17262 17360 17458 17556
##  [3613] 17654 17752 17850 17948 18046 18144 18242 18340 18438 18536 18634 18732
##  [3625] 18830 18928 19026 19124 19222 19320 19418 19516 19614 19712 19810 19908
##  [3637] 20006 20104 20202 20300 20398 20496 20594 20692 20790 20888 20986 21084
##  [3649] 21182 21280 21378 21476 21574 21672 21770 21868 21966 22064 22162 22260
##  [3661] 22358 22456 22554 22652 22750 22848 22946 23044 23142 23240 23338 23436
##  [3673] 23534 23632 23730 23828 23926 24024 24122 24220 24318 24416 24514 24612
##  [3685] 24710 24808 24906 25004 25102 25200 25298 25396 25494 25592 25690 25788
##  [3697]    15   113   211   309   407   505   603   701   799   897   995  1093
##  [3709]  1191  1289  1387  1485  1583  1681  1779  1877  1975  2073  2171  2269
##  [3721]  2367  2465  2563  2661  2759  2857  2955  3053  3151  3249  3347  3445
##  [3733]  3543  3641  3739  3837  3935  4033  4131  4229  4327  4425  4523  4621
##  [3745]  4719  4817  4915  5013  5111  5209  5307  5405  5503  5601  5699  5797
##  [3757]  5895  5993  6091  6189  6287  6385  6483  6581  6679  6777  6875  6973
##  [3769]  7071  7169  7267  7365  7463  7561  7659  7757  7855  7953  8051  8149
##  [3781]  8247  8345  8443  8541  8639  8737  8835  8933  9031  9129  9227  9325
##  [3793]  9423  9521  9619  9717  9815  9913 10011 10109 10207 10305 10403 10501
##  [3805] 10599 10697 10795 10893 10991 11089 11187 11285 11383 11481 11579 11677
##  [3817] 11775 11873 11971 12069 12167 12265 12363 12461 12559 12657 12755 12853
##  [3829] 12951 13049 13147 13245 13343 13441 13539 13637 13735 13833 13931 14029
##  [3841] 14127 14225 14323 14421 14519 14617 14715 14813 14911 15009 15107 15205
##  [3853] 15303 15401 15499 15597 15695 15793 15891 15989 16087 16185 16283 16381
##  [3865] 16479 16577 16675 16773 16871 16969 17067 17165 17263 17361 17459 17557
##  [3877] 17655 17753 17851 17949 18047 18145 18243 18341 18439 18537 18635 18733
##  [3889] 18831 18929 19027 19125 19223 19321 19419 19517 19615 19713 19811 19909
##  [3901] 20007 20105 20203 20301 20399 20497 20595 20693 20791 20889 20987 21085
##  [3913] 21183 21281 21379 21477 21575 21673 21771 21869 21967 22065 22163 22261
##  [3925] 22359 22457 22555 22653 22751 22849 22947 23045 23143 23241 23339 23437
##  [3937] 23535 23633 23731 23829 23927 24025 24123 24221 24319 24417 24515 24613
##  [3949] 24711 24809 24907 25005 25103 25201 25299 25397 25495 25593 25691 25789
##  [3961]    16   114   212   310   408   506   604   702   800   898   996  1094
##  [3973]  1192  1290  1388  1486  1584  1682  1780  1878  1976  2074  2172  2270
##  [3985]  2368  2466  2564  2662  2760  2858  2956  3054  3152  3250  3348  3446
##  [3997]  3544  3642  3740  3838  3936  4034  4132  4230  4328  4426  4524  4622
##  [4009]  4720  4818  4916  5014  5112  5210  5308  5406  5504  5602  5700  5798
##  [4021]  5896  5994  6092  6190  6288  6386  6484  6582  6680  6778  6876  6974
##  [4033]  7072  7170  7268  7366  7464  7562  7660  7758  7856  7954  8052  8150
##  [4045]  8248  8346  8444  8542  8640  8738  8836  8934  9032  9130  9228  9326
##  [4057]  9424  9522  9620  9718  9816  9914 10012 10110 10208 10306 10404 10502
##  [4069] 10600 10698 10796 10894 10992 11090 11188 11286 11384 11482 11580 11678
##  [4081] 11776 11874 11972 12070 12168 12266 12364 12462 12560 12658 12756 12854
##  [4093] 12952 13050 13148 13246 13344 13442 13540 13638 13736 13834 13932 14030
##  [4105] 14128 14226 14324 14422 14520 14618 14716 14814 14912 15010 15108 15206
##  [4117] 15304 15402 15500 15598 15696 15794 15892 15990 16088 16186 16284 16382
##  [4129] 16480 16578 16676 16774 16872 16970 17068 17166 17264 17362 17460 17558
##  [4141] 17656 17754 17852 17950 18048 18146 18244 18342 18440 18538 18636 18734
##  [4153] 18832 18930 19028 19126 19224 19322 19420 19518 19616 19714 19812 19910
##  [4165] 20008 20106 20204 20302 20400 20498 20596 20694 20792 20890 20988 21086
##  [4177] 21184 21282 21380 21478 21576 21674 21772 21870 21968 22066 22164 22262
##  [4189] 22360 22458 22556 22654 22752 22850 22948 23046 23144 23242 23340 23438
##  [4201] 23536 23634 23732 23830 23928 24026 24124 24222 24320 24418 24516 24614
##  [4213] 24712 24810 24908 25006 25104 25202 25300 25398 25496 25594 25692 25790
##  [4225]    17   115   213   311   409   507   605   703   801   899   997  1095
##  [4237]  1193  1291  1389  1487  1585  1683  1781  1879  1977  2075  2173  2271
##  [4249]  2369  2467  2565  2663  2761  2859  2957  3055  3153  3251  3349  3447
##  [4261]  3545  3643  3741  3839  3937  4035  4133  4231  4329  4427  4525  4623
##  [4273]  4721  4819  4917  5015  5113  5211  5309  5407  5505  5603  5701  5799
##  [4285]  5897  5995  6093  6191  6289  6387  6485  6583  6681  6779  6877  6975
##  [4297]  7073  7171  7269  7367  7465  7563  7661  7759  7857  7955  8053  8151
##  [4309]  8249  8347  8445  8543  8641  8739  8837  8935  9033  9131  9229  9327
##  [4321]  9425  9523  9621  9719  9817  9915 10013 10111 10209 10307 10405 10503
##  [4333] 10601 10699 10797 10895 10993 11091 11189 11287 11385 11483 11581 11679
##  [4345] 11777 11875 11973 12071 12169 12267 12365 12463 12561 12659 12757 12855
##  [4357] 12953 13051 13149 13247 13345 13443 13541 13639 13737 13835 13933 14031
##  [4369] 14129 14227 14325 14423 14521 14619 14717 14815 14913 15011 15109 15207
##  [4381] 15305 15403 15501 15599 15697 15795 15893 15991 16089 16187 16285 16383
##  [4393] 16481 16579 16677 16775 16873 16971 17069 17167 17265 17363 17461 17559
##  [4405] 17657 17755 17853 17951 18049 18147 18245 18343 18441 18539 18637 18735
##  [4417] 18833 18931 19029 19127 19225 19323 19421 19519 19617 19715 19813 19911
##  [4429] 20009 20107 20205 20303 20401 20499 20597 20695 20793 20891 20989 21087
##  [4441] 21185 21283 21381 21479 21577 21675 21773 21871 21969 22067 22165 22263
##  [4453] 22361 22459 22557 22655 22753 22851 22949 23047 23145 23243 23341 23439
##  [4465] 23537 23635 23733 23831 23929 24027 24125 24223 24321 24419 24517 24615
##  [4477] 24713 24811 24909 25007 25105 25203 25301 25399 25497 25595 25693 25791
##  [4489]    18   116   214   312   410   508   606   704   802   900   998  1096
##  [4501]  1194  1292  1390  1488  1586  1684  1782  1880  1978  2076  2174  2272
##  [4513]  2370  2468  2566  2664  2762  2860  2958  3056  3154  3252  3350  3448
##  [4525]  3546  3644  3742  3840  3938  4036  4134  4232  4330  4428  4526  4624
##  [4537]  4722  4820  4918  5016  5114  5212  5310  5408  5506  5604  5702  5800
##  [4549]  5898  5996  6094  6192  6290  6388  6486  6584  6682  6780  6878  6976
##  [4561]  7074  7172  7270  7368  7466  7564  7662  7760  7858  7956  8054  8152
##  [4573]  8250  8348  8446  8544  8642  8740  8838  8936  9034  9132  9230  9328
##  [4585]  9426  9524  9622  9720  9818  9916 10014 10112 10210 10308 10406 10504
##  [4597] 10602 10700 10798 10896 10994 11092 11190 11288 11386 11484 11582 11680
##  [4609] 11778 11876 11974 12072 12170 12268 12366 12464 12562 12660 12758 12856
##  [4621] 12954 13052 13150 13248 13346 13444 13542 13640 13738 13836 13934 14032
##  [4633] 14130 14228 14326 14424 14522 14620 14718 14816 14914 15012 15110 15208
##  [4645] 15306 15404 15502 15600 15698 15796 15894 15992 16090 16188 16286 16384
##  [4657] 16482 16580 16678 16776 16874 16972 17070 17168 17266 17364 17462 17560
##  [4669] 17658 17756 17854 17952 18050 18148 18246 18344 18442 18540 18638 18736
##  [4681] 18834 18932 19030 19128 19226 19324 19422 19520 19618 19716 19814 19912
##  [4693] 20010 20108 20206 20304 20402 20500 20598 20696 20794 20892 20990 21088
##  [4705] 21186 21284 21382 21480 21578 21676 21774 21872 21970 22068 22166 22264
##  [4717] 22362 22460 22558 22656 22754 22852 22950 23048 23146 23244 23342 23440
##  [4729] 23538 23636 23734 23832 23930 24028 24126 24224 24322 24420 24518 24616
##  [4741] 24714 24812 24910 25008 25106 25204 25302 25400 25498 25596 25694 25792
##  [4753]    19   117   215   313   411   509   607   705   803   901   999  1097
##  [4765]  1195  1293  1391  1489  1587  1685  1783  1881  1979  2077  2175  2273
##  [4777]  2371  2469  2567  2665  2763  2861  2959  3057  3155  3253  3351  3449
##  [4789]  3547  3645  3743  3841  3939  4037  4135  4233  4331  4429  4527  4625
##  [4801]  4723  4821  4919  5017  5115  5213  5311  5409  5507  5605  5703  5801
##  [4813]  5899  5997  6095  6193  6291  6389  6487  6585  6683  6781  6879  6977
##  [4825]  7075  7173  7271  7369  7467  7565  7663  7761  7859  7957  8055  8153
##  [4837]  8251  8349  8447  8545  8643  8741  8839  8937  9035  9133  9231  9329
##  [4849]  9427  9525  9623  9721  9819  9917 10015 10113 10211 10309 10407 10505
##  [4861] 10603 10701 10799 10897 10995 11093 11191 11289 11387 11485 11583 11681
##  [4873] 11779 11877 11975 12073 12171 12269 12367 12465 12563 12661 12759 12857
##  [4885] 12955 13053 13151 13249 13347 13445 13543 13641 13739 13837 13935 14033
##  [4897] 14131 14229 14327 14425 14523 14621 14719 14817 14915 15013 15111 15209
##  [4909] 15307 15405 15503 15601 15699 15797 15895 15993 16091 16189 16287 16385
##  [4921] 16483 16581 16679 16777 16875 16973 17071 17169 17267 17365 17463 17561
##  [4933] 17659 17757 17855 17953 18051 18149 18247 18345 18443 18541 18639 18737
##  [4945] 18835 18933 19031 19129 19227 19325 19423 19521 19619 19717 19815 19913
##  [4957] 20011 20109 20207 20305 20403 20501 20599 20697 20795 20893 20991 21089
##  [4969] 21187 21285 21383 21481 21579 21677 21775 21873 21971 22069 22167 22265
##  [4981] 22363 22461 22559 22657 22755 22853 22951 23049 23147 23245 23343 23441
##  [4993] 23539 23637 23735 23833 23931 24029 24127 24225 24323 24421 24519 24617
##  [5005] 24715 24813 24911 25009 25107 25205 25303 25401 25499 25597 25695 25793
##  [5017]    20   118   216   314   412   510   608   706   804   902  1000  1098
##  [5029]  1196  1294  1392  1490  1588  1686  1784  1882  1980  2078  2176  2274
##  [5041]  2372  2470  2568  2666  2764  2862  2960  3058  3156  3254  3352  3450
##  [5053]  3548  3646  3744  3842  3940  4038  4136  4234  4332  4430  4528  4626
##  [5065]  4724  4822  4920  5018  5116  5214  5312  5410  5508  5606  5704  5802
##  [5077]  5900  5998  6096  6194  6292  6390  6488  6586  6684  6782  6880  6978
##  [5089]  7076  7174  7272  7370  7468  7566  7664  7762  7860  7958  8056  8154
##  [5101]  8252  8350  8448  8546  8644  8742  8840  8938  9036  9134  9232  9330
##  [5113]  9428  9526  9624  9722  9820  9918 10016 10114 10212 10310 10408 10506
##  [5125] 10604 10702 10800 10898 10996 11094 11192 11290 11388 11486 11584 11682
##  [5137] 11780 11878 11976 12074 12172 12270 12368 12466 12564 12662 12760 12858
##  [5149] 12956 13054 13152 13250 13348 13446 13544 13642 13740 13838 13936 14034
##  [5161] 14132 14230 14328 14426 14524 14622 14720 14818 14916 15014 15112 15210
##  [5173] 15308 15406 15504 15602 15700 15798 15896 15994 16092 16190 16288 16386
##  [5185] 16484 16582 16680 16778 16876 16974 17072 17170 17268 17366 17464 17562
##  [5197] 17660 17758 17856 17954 18052 18150 18248 18346 18444 18542 18640 18738
##  [5209] 18836 18934 19032 19130 19228 19326 19424 19522 19620 19718 19816 19914
##  [5221] 20012 20110 20208 20306 20404 20502 20600 20698 20796 20894 20992 21090
##  [5233] 21188 21286 21384 21482 21580 21678 21776 21874 21972 22070 22168 22266
##  [5245] 22364 22462 22560 22658 22756 22854 22952 23050 23148 23246 23344 23442
##  [5257] 23540 23638 23736 23834 23932 24030 24128 24226 24324 24422 24520 24618
##  [5269] 24716 24814 24912 25010 25108 25206 25304 25402 25500 25598 25696 25794
##  [5281]    21   119   217   315   413   511   609   707   805   903  1001  1099
##  [5293]  1197  1295  1393  1491  1589  1687  1785  1883  1981  2079  2177  2275
##  [5305]  2373  2471  2569  2667  2765  2863  2961  3059  3157  3255  3353  3451
##  [5317]  3549  3647  3745  3843  3941  4039  4137  4235  4333  4431  4529  4627
##  [5329]  4725  4823  4921  5019  5117  5215  5313  5411  5509  5607  5705  5803
##  [5341]  5901  5999  6097  6195  6293  6391  6489  6587  6685  6783  6881  6979
##  [5353]  7077  7175  7273  7371  7469  7567  7665  7763  7861  7959  8057  8155
##  [5365]  8253  8351  8449  8547  8645  8743  8841  8939  9037  9135  9233  9331
##  [5377]  9429  9527  9625  9723  9821  9919 10017 10115 10213 10311 10409 10507
##  [5389] 10605 10703 10801 10899 10997 11095 11193 11291 11389 11487 11585 11683
##  [5401] 11781 11879 11977 12075 12173 12271 12369 12467 12565 12663 12761 12859
##  [5413] 12957 13055 13153 13251 13349 13447 13545 13643 13741 13839 13937 14035
##  [5425] 14133 14231 14329 14427 14525 14623 14721 14819 14917 15015 15113 15211
##  [5437] 15309 15407 15505 15603 15701 15799 15897 15995 16093 16191 16289 16387
##  [5449] 16485 16583 16681 16779 16877 16975 17073 17171 17269 17367 17465 17563
##  [5461] 17661 17759 17857 17955 18053 18151 18249 18347 18445 18543 18641 18739
##  [5473] 18837 18935 19033 19131 19229 19327 19425 19523 19621 19719 19817 19915
##  [5485] 20013 20111 20209 20307 20405 20503 20601 20699 20797 20895 20993 21091
##  [5497] 21189 21287 21385 21483 21581 21679 21777 21875 21973 22071 22169 22267
##  [5509] 22365 22463 22561 22659 22757 22855 22953 23051 23149 23247 23345 23443
##  [5521] 23541 23639 23737 23835 23933 24031 24129 24227 24325 24423 24521 24619
##  [5533] 24717 24815 24913 25011 25109 25207 25305 25403 25501 25599 25697 25795
##  [5545]    22   120   218   316   414   512   610   708   806   904  1002  1100
##  [5557]  1198  1296  1394  1492  1590  1688  1786  1884  1982  2080  2178  2276
##  [5569]  2374  2472  2570  2668  2766  2864  2962  3060  3158  3256  3354  3452
##  [5581]  3550  3648  3746  3844  3942  4040  4138  4236  4334  4432  4530  4628
##  [5593]  4726  4824  4922  5020  5118  5216  5314  5412  5510  5608  5706  5804
##  [5605]  5902  6000  6098  6196  6294  6392  6490  6588  6686  6784  6882  6980
##  [5617]  7078  7176  7274  7372  7470  7568  7666  7764  7862  7960  8058  8156
##  [5629]  8254  8352  8450  8548  8646  8744  8842  8940  9038  9136  9234  9332
##  [5641]  9430  9528  9626  9724  9822  9920 10018 10116 10214 10312 10410 10508
##  [5653] 10606 10704 10802 10900 10998 11096 11194 11292 11390 11488 11586 11684
##  [5665] 11782 11880 11978 12076 12174 12272 12370 12468 12566 12664 12762 12860
##  [5677] 12958 13056 13154 13252 13350 13448 13546 13644 13742 13840 13938 14036
##  [5689] 14134 14232 14330 14428 14526 14624 14722 14820 14918 15016 15114 15212
##  [5701] 15310 15408 15506 15604 15702 15800 15898 15996 16094 16192 16290 16388
##  [5713] 16486 16584 16682 16780 16878 16976 17074 17172 17270 17368 17466 17564
##  [5725] 17662 17760 17858 17956 18054 18152 18250 18348 18446 18544 18642 18740
##  [5737] 18838 18936 19034 19132 19230 19328 19426 19524 19622 19720 19818 19916
##  [5749] 20014 20112 20210 20308 20406 20504 20602 20700 20798 20896 20994 21092
##  [5761] 21190 21288 21386 21484 21582 21680 21778 21876 21974 22072 22170 22268
##  [5773] 22366 22464 22562 22660 22758 22856 22954 23052 23150 23248 23346 23444
##  [5785] 23542 23640 23738 23836 23934 24032 24130 24228 24326 24424 24522 24620
##  [5797] 24718 24816 24914 25012 25110 25208 25306 25404 25502 25600 25698 25796
##  [5809]    23   121   219   317   415   513   611   709   807   905  1003  1101
##  [5821]  1199  1297  1395  1493  1591  1689  1787  1885  1983  2081  2179  2277
##  [5833]  2375  2473  2571  2669  2767  2865  2963  3061  3159  3257  3355  3453
##  [5845]  3551  3649  3747  3845  3943  4041  4139  4237  4335  4433  4531  4629
##  [5857]  4727  4825  4923  5021  5119  5217  5315  5413  5511  5609  5707  5805
##  [5869]  5903  6001  6099  6197  6295  6393  6491  6589  6687  6785  6883  6981
##  [5881]  7079  7177  7275  7373  7471  7569  7667  7765  7863  7961  8059  8157
##  [5893]  8255  8353  8451  8549  8647  8745  8843  8941  9039  9137  9235  9333
##  [5905]  9431  9529  9627  9725  9823  9921 10019 10117 10215 10313 10411 10509
##  [5917] 10607 10705 10803 10901 10999 11097 11195 11293 11391 11489 11587 11685
##  [5929] 11783 11881 11979 12077 12175 12273 12371 12469 12567 12665 12763 12861
##  [5941] 12959 13057 13155 13253 13351 13449 13547 13645 13743 13841 13939 14037
##  [5953] 14135 14233 14331 14429 14527 14625 14723 14821 14919 15017 15115 15213
##  [5965] 15311 15409 15507 15605 15703 15801 15899 15997 16095 16193 16291 16389
##  [5977] 16487 16585 16683 16781 16879 16977 17075 17173 17271 17369 17467 17565
##  [5989] 17663 17761 17859 17957 18055 18153 18251 18349 18447 18545 18643 18741
##  [6001] 18839 18937 19035 19133 19231 19329 19427 19525 19623 19721 19819 19917
##  [6013] 20015 20113 20211 20309 20407 20505 20603 20701 20799 20897 20995 21093
##  [6025] 21191 21289 21387 21485 21583 21681 21779 21877 21975 22073 22171 22269
##  [6037] 22367 22465 22563 22661 22759 22857 22955 23053 23151 23249 23347 23445
##  [6049] 23543 23641 23739 23837 23935 24033 24131 24229 24327 24425 24523 24621
##  [6061] 24719 24817 24915 25013 25111 25209 25307 25405 25503 25601 25699 25797
##  [6073]    24   122   220   318   416   514   612   710   808   906  1004  1102
##  [6085]  1200  1298  1396  1494  1592  1690  1788  1886  1984  2082  2180  2278
##  [6097]  2376  2474  2572  2670  2768  2866  2964  3062  3160  3258  3356  3454
##  [6109]  3552  3650  3748  3846  3944  4042  4140  4238  4336  4434  4532  4630
##  [6121]  4728  4826  4924  5022  5120  5218  5316  5414  5512  5610  5708  5806
##  [6133]  5904  6002  6100  6198  6296  6394  6492  6590  6688  6786  6884  6982
##  [6145]  7080  7178  7276  7374  7472  7570  7668  7766  7864  7962  8060  8158
##  [6157]  8256  8354  8452  8550  8648  8746  8844  8942  9040  9138  9236  9334
##  [6169]  9432  9530  9628  9726  9824  9922 10020 10118 10216 10314 10412 10510
##  [6181] 10608 10706 10804 10902 11000 11098 11196 11294 11392 11490 11588 11686
##  [6193] 11784 11882 11980 12078 12176 12274 12372 12470 12568 12666 12764 12862
##  [6205] 12960 13058 13156 13254 13352 13450 13548 13646 13744 13842 13940 14038
##  [6217] 14136 14234 14332 14430 14528 14626 14724 14822 14920 15018 15116 15214
##  [6229] 15312 15410 15508 15606 15704 15802 15900 15998 16096 16194 16292 16390
##  [6241] 16488 16586 16684 16782 16880 16978 17076 17174 17272 17370 17468 17566
##  [6253] 17664 17762 17860 17958 18056 18154 18252 18350 18448 18546 18644 18742
##  [6265] 18840 18938 19036 19134 19232 19330 19428 19526 19624 19722 19820 19918
##  [6277] 20016 20114 20212 20310 20408 20506 20604 20702 20800 20898 20996 21094
##  [6289] 21192 21290 21388 21486 21584 21682 21780 21878 21976 22074 22172 22270
##  [6301] 22368 22466 22564 22662 22760 22858 22956 23054 23152 23250 23348 23446
##  [6313] 23544 23642 23740 23838 23936 24034 24132 24230 24328 24426 24524 24622
##  [6325] 24720 24818 24916 25014 25112 25210 25308 25406 25504 25602 25700 25798
##  [6337]    25   123   221   319   417   515   613   711   809   907  1005  1103
##  [6349]  1201  1299  1397  1495  1593  1691  1789  1887  1985  2083  2181  2279
##  [6361]  2377  2475  2573  2671  2769  2867  2965  3063  3161  3259  3357  3455
##  [6373]  3553  3651  3749  3847  3945  4043  4141  4239  4337  4435  4533  4631
##  [6385]  4729  4827  4925  5023  5121  5219  5317  5415  5513  5611  5709  5807
##  [6397]  5905  6003  6101  6199  6297  6395  6493  6591  6689  6787  6885  6983
##  [6409]  7081  7179  7277  7375  7473  7571  7669  7767  7865  7963  8061  8159
##  [6421]  8257  8355  8453  8551  8649  8747  8845  8943  9041  9139  9237  9335
##  [6433]  9433  9531  9629  9727  9825  9923 10021 10119 10217 10315 10413 10511
##  [6445] 10609 10707 10805 10903 11001 11099 11197 11295 11393 11491 11589 11687
##  [6457] 11785 11883 11981 12079 12177 12275 12373 12471 12569 12667 12765 12863
##  [6469] 12961 13059 13157 13255 13353 13451 13549 13647 13745 13843 13941 14039
##  [6481] 14137 14235 14333 14431 14529 14627 14725 14823 14921 15019 15117 15215
##  [6493] 15313 15411 15509 15607 15705 15803 15901 15999 16097 16195 16293 16391
##  [6505] 16489 16587 16685 16783 16881 16979 17077 17175 17273 17371 17469 17567
##  [6517] 17665 17763 17861 17959 18057 18155 18253 18351 18449 18547 18645 18743
##  [6529] 18841 18939 19037 19135 19233 19331 19429 19527 19625 19723 19821 19919
##  [6541] 20017 20115 20213 20311 20409 20507 20605 20703 20801 20899 20997 21095
##  [6553] 21193 21291 21389 21487 21585 21683 21781 21879 21977 22075 22173 22271
##  [6565] 22369 22467 22565 22663 22761 22859 22957 23055 23153 23251 23349 23447
##  [6577] 23545 23643 23741 23839 23937 24035 24133 24231 24329 24427 24525 24623
##  [6589] 24721 24819 24917 25015 25113 25211 25309 25407 25505 25603 25701 25799
##  [6601]    26   124   222   320   418   516   614   712   810   908  1006  1104
##  [6613]  1202  1300  1398  1496  1594  1692  1790  1888  1986  2084  2182  2280
##  [6625]  2378  2476  2574  2672  2770  2868  2966  3064  3162  3260  3358  3456
##  [6637]  3554  3652  3750  3848  3946  4044  4142  4240  4338  4436  4534  4632
##  [6649]  4730  4828  4926  5024  5122  5220  5318  5416  5514  5612  5710  5808
##  [6661]  5906  6004  6102  6200  6298  6396  6494  6592  6690  6788  6886  6984
##  [6673]  7082  7180  7278  7376  7474  7572  7670  7768  7866  7964  8062  8160
##  [6685]  8258  8356  8454  8552  8650  8748  8846  8944  9042  9140  9238  9336
##  [6697]  9434  9532  9630  9728  9826  9924 10022 10120 10218 10316 10414 10512
##  [6709] 10610 10708 10806 10904 11002 11100 11198 11296 11394 11492 11590 11688
##  [6721] 11786 11884 11982 12080 12178 12276 12374 12472 12570 12668 12766 12864
##  [6733] 12962 13060 13158 13256 13354 13452 13550 13648 13746 13844 13942 14040
##  [6745] 14138 14236 14334 14432 14530 14628 14726 14824 14922 15020 15118 15216
##  [6757] 15314 15412 15510 15608 15706 15804 15902 16000 16098 16196 16294 16392
##  [6769] 16490 16588 16686 16784 16882 16980 17078 17176 17274 17372 17470 17568
##  [6781] 17666 17764 17862 17960 18058 18156 18254 18352 18450 18548 18646 18744
##  [6793] 18842 18940 19038 19136 19234 19332 19430 19528 19626 19724 19822 19920
##  [6805] 20018 20116 20214 20312 20410 20508 20606 20704 20802 20900 20998 21096
##  [6817] 21194 21292 21390 21488 21586 21684 21782 21880 21978 22076 22174 22272
##  [6829] 22370 22468 22566 22664 22762 22860 22958 23056 23154 23252 23350 23448
##  [6841] 23546 23644 23742 23840 23938 24036 24134 24232 24330 24428 24526 24624
##  [6853] 24722 24820 24918 25016 25114 25212 25310 25408 25506 25604 25702 25800
##  [6865]    27   125   223   321   419   517   615   713   811   909  1007  1105
##  [6877]  1203  1301  1399  1497  1595  1693  1791  1889  1987  2085  2183  2281
##  [6889]  2379  2477  2575  2673  2771  2869  2967  3065  3163  3261  3359  3457
##  [6901]  3555  3653  3751  3849  3947  4045  4143  4241  4339  4437  4535  4633
##  [6913]  4731  4829  4927  5025  5123  5221  5319  5417  5515  5613  5711  5809
##  [6925]  5907  6005  6103  6201  6299  6397  6495  6593  6691  6789  6887  6985
##  [6937]  7083  7181  7279  7377  7475  7573  7671  7769  7867  7965  8063  8161
##  [6949]  8259  8357  8455  8553  8651  8749  8847  8945  9043  9141  9239  9337
##  [6961]  9435  9533  9631  9729  9827  9925 10023 10121 10219 10317 10415 10513
##  [6973] 10611 10709 10807 10905 11003 11101 11199 11297 11395 11493 11591 11689
##  [6985] 11787 11885 11983 12081 12179 12277 12375 12473 12571 12669 12767 12865
##  [6997] 12963 13061 13159 13257 13355 13453 13551 13649 13747 13845 13943 14041
##  [7009] 14139 14237 14335 14433 14531 14629 14727 14825 14923 15021 15119 15217
##  [7021] 15315 15413 15511 15609 15707 15805 15903 16001 16099 16197 16295 16393
##  [7033] 16491 16589 16687 16785 16883 16981 17079 17177 17275 17373 17471 17569
##  [7045] 17667 17765 17863 17961 18059 18157 18255 18353 18451 18549 18647 18745
##  [7057] 18843 18941 19039 19137 19235 19333 19431 19529 19627 19725 19823 19921
##  [7069] 20019 20117 20215 20313 20411 20509 20607 20705 20803 20901 20999 21097
##  [7081] 21195 21293 21391 21489 21587 21685 21783 21881 21979 22077 22175 22273
##  [7093] 22371 22469 22567 22665 22763 22861 22959 23057 23155 23253 23351 23449
##  [7105] 23547 23645 23743 23841 23939 24037 24135 24233 24331 24429 24527 24625
##  [7117] 24723 24821 24919 25017 25115 25213 25311 25409 25507 25605 25703 25801
##  [7129]    28   126   224   322   420   518   616   714   812   910  1008  1106
##  [7141]  1204  1302  1400  1498  1596  1694  1792  1890  1988  2086  2184  2282
##  [7153]  2380  2478  2576  2674  2772  2870  2968  3066  3164  3262  3360  3458
##  [7165]  3556  3654  3752  3850  3948  4046  4144  4242  4340  4438  4536  4634
##  [7177]  4732  4830  4928  5026  5124  5222  5320  5418  5516  5614  5712  5810
##  [7189]  5908  6006  6104  6202  6300  6398  6496  6594  6692  6790  6888  6986
##  [7201]  7084  7182  7280  7378  7476  7574  7672  7770  7868  7966  8064  8162
##  [7213]  8260  8358  8456  8554  8652  8750  8848  8946  9044  9142  9240  9338
##  [7225]  9436  9534  9632  9730  9828  9926 10024 10122 10220 10318 10416 10514
##  [7237] 10612 10710 10808 10906 11004 11102 11200 11298 11396 11494 11592 11690
##  [7249] 11788 11886 11984 12082 12180 12278 12376 12474 12572 12670 12768 12866
##  [7261] 12964 13062 13160 13258 13356 13454 13552 13650 13748 13846 13944 14042
##  [7273] 14140 14238 14336 14434 14532 14630 14728 14826 14924 15022 15120 15218
##  [7285] 15316 15414 15512 15610 15708 15806 15904 16002 16100 16198 16296 16394
##  [7297] 16492 16590 16688 16786 16884 16982 17080 17178 17276 17374 17472 17570
##  [7309] 17668 17766 17864 17962 18060 18158 18256 18354 18452 18550 18648 18746
##  [7321] 18844 18942 19040 19138 19236 19334 19432 19530 19628 19726 19824 19922
##  [7333] 20020 20118 20216 20314 20412 20510 20608 20706 20804 20902 21000 21098
##  [7345] 21196 21294 21392 21490 21588 21686 21784 21882 21980 22078 22176 22274
##  [7357] 22372 22470 22568 22666 22764 22862 22960 23058 23156 23254 23352 23450
##  [7369] 23548 23646 23744 23842 23940 24038 24136 24234 24332 24430 24528 24626
##  [7381] 24724 24822 24920 25018 25116 25214 25312 25410 25508 25606 25704 25802
##  [7393]    29   127   225   323   421   519   617   715   813   911  1009  1107
##  [7405]  1205  1303  1401  1499  1597  1695  1793  1891  1989  2087  2185  2283
##  [7417]  2381  2479  2577  2675  2773  2871  2969  3067  3165  3263  3361  3459
##  [7429]  3557  3655  3753  3851  3949  4047  4145  4243  4341  4439  4537  4635
##  [7441]  4733  4831  4929  5027  5125  5223  5321  5419  5517  5615  5713  5811
##  [7453]  5909  6007  6105  6203  6301  6399  6497  6595  6693  6791  6889  6987
##  [7465]  7085  7183  7281  7379  7477  7575  7673  7771  7869  7967  8065  8163
##  [7477]  8261  8359  8457  8555  8653  8751  8849  8947  9045  9143  9241  9339
##  [7489]  9437  9535  9633  9731  9829  9927 10025 10123 10221 10319 10417 10515
##  [7501] 10613 10711 10809 10907 11005 11103 11201 11299 11397 11495 11593 11691
##  [7513] 11789 11887 11985 12083 12181 12279 12377 12475 12573 12671 12769 12867
##  [7525] 12965 13063 13161 13259 13357 13455 13553 13651 13749 13847 13945 14043
##  [7537] 14141 14239 14337 14435 14533 14631 14729 14827 14925 15023 15121 15219
##  [7549] 15317 15415 15513 15611 15709 15807 15905 16003 16101 16199 16297 16395
##  [7561] 16493 16591 16689 16787 16885 16983 17081 17179 17277 17375 17473 17571
##  [7573] 17669 17767 17865 17963 18061 18159 18257 18355 18453 18551 18649 18747
##  [7585] 18845 18943 19041 19139 19237 19335 19433 19531 19629 19727 19825 19923
##  [7597] 20021 20119 20217 20315 20413 20511 20609 20707 20805 20903 21001 21099
##  [7609] 21197 21295 21393 21491 21589 21687 21785 21883 21981 22079 22177 22275
##  [7621] 22373 22471 22569 22667 22765 22863 22961 23059 23157 23255 23353 23451
##  [7633] 23549 23647 23745 23843 23941 24039 24137 24235 24333 24431 24529 24627
##  [7645] 24725 24823 24921 25019 25117 25215 25313 25411 25509 25607 25705 25803
##  [7657]    30   128   226   324   422   520   618   716   814   912  1010  1108
##  [7669]  1206  1304  1402  1500  1598  1696  1794  1892  1990  2088  2186  2284
##  [7681]  2382  2480  2578  2676  2774  2872  2970  3068  3166  3264  3362  3460
##  [7693]  3558  3656  3754  3852  3950  4048  4146  4244  4342  4440  4538  4636
##  [7705]  4734  4832  4930  5028  5126  5224  5322  5420  5518  5616  5714  5812
##  [7717]  5910  6008  6106  6204  6302  6400  6498  6596  6694  6792  6890  6988
##  [7729]  7086  7184  7282  7380  7478  7576  7674  7772  7870  7968  8066  8164
##  [7741]  8262  8360  8458  8556  8654  8752  8850  8948  9046  9144  9242  9340
##  [7753]  9438  9536  9634  9732  9830  9928 10026 10124 10222 10320 10418 10516
##  [7765] 10614 10712 10810 10908 11006 11104 11202 11300 11398 11496 11594 11692
##  [7777] 11790 11888 11986 12084 12182 12280 12378 12476 12574 12672 12770 12868
##  [7789] 12966 13064 13162 13260 13358 13456 13554 13652 13750 13848 13946 14044
##  [7801] 14142 14240 14338 14436 14534 14632 14730 14828 14926 15024 15122 15220
##  [7813] 15318 15416 15514 15612 15710 15808 15906 16004 16102 16200 16298 16396
##  [7825] 16494 16592 16690 16788 16886 16984 17082 17180 17278 17376 17474 17572
##  [7837] 17670 17768 17866 17964 18062 18160 18258 18356 18454 18552 18650 18748
##  [7849] 18846 18944 19042 19140 19238 19336 19434 19532 19630 19728 19826 19924
##  [7861] 20022 20120 20218 20316 20414 20512 20610 20708 20806 20904 21002 21100
##  [7873] 21198 21296 21394 21492 21590 21688 21786 21884 21982 22080 22178 22276
##  [7885] 22374 22472 22570 22668 22766 22864 22962 23060 23158 23256 23354 23452
##  [7897] 23550 23648 23746 23844 23942 24040 24138 24236 24334 24432 24530 24628
##  [7909] 24726 24824 24922 25020 25118 25216 25314 25412 25510 25608 25706 25804
##  [7921]    31   129   227   325   423   521   619   717   815   913  1011  1109
##  [7933]  1207  1305  1403  1501  1599  1697  1795  1893  1991  2089  2187  2285
##  [7945]  2383  2481  2579  2677  2775  2873  2971  3069  3167  3265  3363  3461
##  [7957]  3559  3657  3755  3853  3951  4049  4147  4245  4343  4441  4539  4637
##  [7969]  4735  4833  4931  5029  5127  5225  5323  5421  5519  5617  5715  5813
##  [7981]  5911  6009  6107  6205  6303  6401  6499  6597  6695  6793  6891  6989
##  [7993]  7087  7185  7283  7381  7479  7577  7675  7773  7871  7969  8067  8165
##  [8005]  8263  8361  8459  8557  8655  8753  8851  8949  9047  9145  9243  9341
##  [8017]  9439  9537  9635  9733  9831  9929 10027 10125 10223 10321 10419 10517
##  [8029] 10615 10713 10811 10909 11007 11105 11203 11301 11399 11497 11595 11693
##  [8041] 11791 11889 11987 12085 12183 12281 12379 12477 12575 12673 12771 12869
##  [8053] 12967 13065 13163 13261 13359 13457 13555 13653 13751 13849 13947 14045
##  [8065] 14143 14241 14339 14437 14535 14633 14731 14829 14927 15025 15123 15221
##  [8077] 15319 15417 15515 15613 15711 15809 15907 16005 16103 16201 16299 16397
##  [8089] 16495 16593 16691 16789 16887 16985 17083 17181 17279 17377 17475 17573
##  [8101] 17671 17769 17867 17965 18063 18161 18259 18357 18455 18553 18651 18749
##  [8113] 18847 18945 19043 19141 19239 19337 19435 19533 19631 19729 19827 19925
##  [8125] 20023 20121 20219 20317 20415 20513 20611 20709 20807 20905 21003 21101
##  [8137] 21199 21297 21395 21493 21591 21689 21787 21885 21983 22081 22179 22277
##  [8149] 22375 22473 22571 22669 22767 22865 22963 23061 23159 23257 23355 23453
##  [8161] 23551 23649 23747 23845 23943 24041 24139 24237 24335 24433 24531 24629
##  [8173] 24727 24825 24923 25021 25119 25217 25315 25413 25511 25609 25707 25805
##  [8185]    32   130   228   326   424   522   620   718   816   914  1012  1110
##  [8197]  1208  1306  1404  1502  1600  1698  1796  1894  1992  2090  2188  2286
##  [8209]  2384  2482  2580  2678  2776  2874  2972  3070  3168  3266  3364  3462
##  [8221]  3560  3658  3756  3854  3952  4050  4148  4246  4344  4442  4540  4638
##  [8233]  4736  4834  4932  5030  5128  5226  5324  5422  5520  5618  5716  5814
##  [8245]  5912  6010  6108  6206  6304  6402  6500  6598  6696  6794  6892  6990
##  [8257]  7088  7186  7284  7382  7480  7578  7676  7774  7872  7970  8068  8166
##  [8269]  8264  8362  8460  8558  8656  8754  8852  8950  9048  9146  9244  9342
##  [8281]  9440  9538  9636  9734  9832  9930 10028 10126 10224 10322 10420 10518
##  [8293] 10616 10714 10812 10910 11008 11106 11204 11302 11400 11498 11596 11694
##  [8305] 11792 11890 11988 12086 12184 12282 12380 12478 12576 12674 12772 12870
##  [8317] 12968 13066 13164 13262 13360 13458 13556 13654 13752 13850 13948 14046
##  [8329] 14144 14242 14340 14438 14536 14634 14732 14830 14928 15026 15124 15222
##  [8341] 15320 15418 15516 15614 15712 15810 15908 16006 16104 16202 16300 16398
##  [8353] 16496 16594 16692 16790 16888 16986 17084 17182 17280 17378 17476 17574
##  [8365] 17672 17770 17868 17966 18064 18162 18260 18358 18456 18554 18652 18750
##  [8377] 18848 18946 19044 19142 19240 19338 19436 19534 19632 19730 19828 19926
##  [8389] 20024 20122 20220 20318 20416 20514 20612 20710 20808 20906 21004 21102
##  [8401] 21200 21298 21396 21494 21592 21690 21788 21886 21984 22082 22180 22278
##  [8413] 22376 22474 22572 22670 22768 22866 22964 23062 23160 23258 23356 23454
##  [8425] 23552 23650 23748 23846 23944 24042 24140 24238 24336 24434 24532 24630
##  [8437] 24728 24826 24924 25022 25120 25218 25316 25414 25512 25610 25708 25806
##  [8449]    33   131   229   327   425   523   621   719   817   915  1013  1111
##  [8461]  1209  1307  1405  1503  1601  1699  1797  1895  1993  2091  2189  2287
##  [8473]  2385  2483  2581  2679  2777  2875  2973  3071  3169  3267  3365  3463
##  [8485]  3561  3659  3757  3855  3953  4051  4149  4247  4345  4443  4541  4639
##  [8497]  4737  4835  4933  5031  5129  5227  5325  5423  5521  5619  5717  5815
##  [8509]  5913  6011  6109  6207  6305  6403  6501  6599  6697  6795  6893  6991
##  [8521]  7089  7187  7285  7383  7481  7579  7677  7775  7873  7971  8069  8167
##  [8533]  8265  8363  8461  8559  8657  8755  8853  8951  9049  9147  9245  9343
##  [8545]  9441  9539  9637  9735  9833  9931 10029 10127 10225 10323 10421 10519
##  [8557] 10617 10715 10813 10911 11009 11107 11205 11303 11401 11499 11597 11695
##  [8569] 11793 11891 11989 12087 12185 12283 12381 12479 12577 12675 12773 12871
##  [8581] 12969 13067 13165 13263 13361 13459 13557 13655 13753 13851 13949 14047
##  [8593] 14145 14243 14341 14439 14537 14635 14733 14831 14929 15027 15125 15223
##  [8605] 15321 15419 15517 15615 15713 15811 15909 16007 16105 16203 16301 16399
##  [8617] 16497 16595 16693 16791 16889 16987 17085 17183 17281 17379 17477 17575
##  [8629] 17673 17771 17869 17967 18065 18163 18261 18359 18457 18555 18653 18751
##  [8641] 18849 18947 19045 19143 19241 19339 19437 19535 19633 19731 19829 19927
##  [8653] 20025 20123 20221 20319 20417 20515 20613 20711 20809 20907 21005 21103
##  [8665] 21201 21299 21397 21495 21593 21691 21789 21887 21985 22083 22181 22279
##  [8677] 22377 22475 22573 22671 22769 22867 22965 23063 23161 23259 23357 23455
##  [8689] 23553 23651 23749 23847 23945 24043 24141 24239 24337 24435 24533 24631
##  [8701] 24729 24827 24925 25023 25121 25219 25317 25415 25513 25611 25709 25807
##  [8713]    34   132   230   328   426   524   622   720   818   916  1014  1112
##  [8725]  1210  1308  1406  1504  1602  1700  1798  1896  1994  2092  2190  2288
##  [8737]  2386  2484  2582  2680  2778  2876  2974  3072  3170  3268  3366  3464
##  [8749]  3562  3660  3758  3856  3954  4052  4150  4248  4346  4444  4542  4640
##  [8761]  4738  4836  4934  5032  5130  5228  5326  5424  5522  5620  5718  5816
##  [8773]  5914  6012  6110  6208  6306  6404  6502  6600  6698  6796  6894  6992
##  [8785]  7090  7188  7286  7384  7482  7580  7678  7776  7874  7972  8070  8168
##  [8797]  8266  8364  8462  8560  8658  8756  8854  8952  9050  9148  9246  9344
##  [8809]  9442  9540  9638  9736  9834  9932 10030 10128 10226 10324 10422 10520
##  [8821] 10618 10716 10814 10912 11010 11108 11206 11304 11402 11500 11598 11696
##  [8833] 11794 11892 11990 12088 12186 12284 12382 12480 12578 12676 12774 12872
##  [8845] 12970 13068 13166 13264 13362 13460 13558 13656 13754 13852 13950 14048
##  [8857] 14146 14244 14342 14440 14538 14636 14734 14832 14930 15028 15126 15224
##  [8869] 15322 15420 15518 15616 15714 15812 15910 16008 16106 16204 16302 16400
##  [8881] 16498 16596 16694 16792 16890 16988 17086 17184 17282 17380 17478 17576
##  [8893] 17674 17772 17870 17968 18066 18164 18262 18360 18458 18556 18654 18752
##  [8905] 18850 18948 19046 19144 19242 19340 19438 19536 19634 19732 19830 19928
##  [8917] 20026 20124 20222 20320 20418 20516 20614 20712 20810 20908 21006 21104
##  [8929] 21202 21300 21398 21496 21594 21692 21790 21888 21986 22084 22182 22280
##  [8941] 22378 22476 22574 22672 22770 22868 22966 23064 23162 23260 23358 23456
##  [8953] 23554 23652 23750 23848 23946 24044 24142 24240 24338 24436 24534 24632
##  [8965] 24730 24828 24926 25024 25122 25220 25318 25416 25514 25612 25710 25808
##  [8977]    35   133   231   329   427   525   623   721   819   917  1015  1113
##  [8989]  1211  1309  1407  1505  1603  1701  1799  1897  1995  2093  2191  2289
##  [9001]  2387  2485  2583  2681  2779  2877  2975  3073  3171  3269  3367  3465
##  [9013]  3563  3661  3759  3857  3955  4053  4151  4249  4347  4445  4543  4641
##  [9025]  4739  4837  4935  5033  5131  5229  5327  5425  5523  5621  5719  5817
##  [9037]  5915  6013  6111  6209  6307  6405  6503  6601  6699  6797  6895  6993
##  [9049]  7091  7189  7287  7385  7483  7581  7679  7777  7875  7973  8071  8169
##  [9061]  8267  8365  8463  8561  8659  8757  8855  8953  9051  9149  9247  9345
##  [9073]  9443  9541  9639  9737  9835  9933 10031 10129 10227 10325 10423 10521
##  [9085] 10619 10717 10815 10913 11011 11109 11207 11305 11403 11501 11599 11697
##  [9097] 11795 11893 11991 12089 12187 12285 12383 12481 12579 12677 12775 12873
##  [9109] 12971 13069 13167 13265 13363 13461 13559 13657 13755 13853 13951 14049
##  [9121] 14147 14245 14343 14441 14539 14637 14735 14833 14931 15029 15127 15225
##  [9133] 15323 15421 15519 15617 15715 15813 15911 16009 16107 16205 16303 16401
##  [9145] 16499 16597 16695 16793 16891 16989 17087 17185 17283 17381 17479 17577
##  [9157] 17675 17773 17871 17969 18067 18165 18263 18361 18459 18557 18655 18753
##  [9169] 18851 18949 19047 19145 19243 19341 19439 19537 19635 19733 19831 19929
##  [9181] 20027 20125 20223 20321 20419 20517 20615 20713 20811 20909 21007 21105
##  [9193] 21203 21301 21399 21497 21595 21693 21791 21889 21987 22085 22183 22281
##  [9205] 22379 22477 22575 22673 22771 22869 22967 23065 23163 23261 23359 23457
##  [9217] 23555 23653 23751 23849 23947 24045 24143 24241 24339 24437 24535 24633
##  [9229] 24731 24829 24927 25025 25123 25221 25319 25417 25515 25613 25711 25809
##  [9241]    36   134   232   330   428   526   624   722   820   918  1016  1114
##  [9253]  1212  1310  1408  1506  1604  1702  1800  1898  1996  2094  2192  2290
##  [9265]  2388  2486  2584  2682  2780  2878  2976  3074  3172  3270  3368  3466
##  [9277]  3564  3662  3760  3858  3956  4054  4152  4250  4348  4446  4544  4642
##  [9289]  4740  4838  4936  5034  5132  5230  5328  5426  5524  5622  5720  5818
##  [9301]  5916  6014  6112  6210  6308  6406  6504  6602  6700  6798  6896  6994
##  [9313]  7092  7190  7288  7386  7484  7582  7680  7778  7876  7974  8072  8170
##  [9325]  8268  8366  8464  8562  8660  8758  8856  8954  9052  9150  9248  9346
##  [9337]  9444  9542  9640  9738  9836  9934 10032 10130 10228 10326 10424 10522
##  [9349] 10620 10718 10816 10914 11012 11110 11208 11306 11404 11502 11600 11698
##  [9361] 11796 11894 11992 12090 12188 12286 12384 12482 12580 12678 12776 12874
##  [9373] 12972 13070 13168 13266 13364 13462 13560 13658 13756 13854 13952 14050
##  [9385] 14148 14246 14344 14442 14540 14638 14736 14834 14932 15030 15128 15226
##  [9397] 15324 15422 15520 15618 15716 15814 15912 16010 16108 16206 16304 16402
##  [9409] 16500 16598 16696 16794 16892 16990 17088 17186 17284 17382 17480 17578
##  [9421] 17676 17774 17872 17970 18068 18166 18264 18362 18460 18558 18656 18754
##  [9433] 18852 18950 19048 19146 19244 19342 19440 19538 19636 19734 19832 19930
##  [9445] 20028 20126 20224 20322 20420 20518 20616 20714 20812 20910 21008 21106
##  [9457] 21204 21302 21400 21498 21596 21694 21792 21890 21988 22086 22184 22282
##  [9469] 22380 22478 22576 22674 22772 22870 22968 23066 23164 23262 23360 23458
##  [9481] 23556 23654 23752 23850 23948 24046 24144 24242 24340 24438 24536 24634
##  [9493] 24732 24830 24928 25026 25124 25222 25320 25418 25516 25614 25712 25810
##  [9505]    37   135   233   331   429   527   625   723   821   919  1017  1115
##  [9517]  1213  1311  1409  1507  1605  1703  1801  1899  1997  2095  2193  2291
##  [9529]  2389  2487  2585  2683  2781  2879  2977  3075  3173  3271  3369  3467
##  [9541]  3565  3663  3761  3859  3957  4055  4153  4251  4349  4447  4545  4643
##  [9553]  4741  4839  4937  5035  5133  5231  5329  5427  5525  5623  5721  5819
##  [9565]  5917  6015  6113  6211  6309  6407  6505  6603  6701  6799  6897  6995
##  [9577]  7093  7191  7289  7387  7485  7583  7681  7779  7877  7975  8073  8171
##  [9589]  8269  8367  8465  8563  8661  8759  8857  8955  9053  9151  9249  9347
##  [9601]  9445  9543  9641  9739  9837  9935 10033 10131 10229 10327 10425 10523
##  [9613] 10621 10719 10817 10915 11013 11111 11209 11307 11405 11503 11601 11699
##  [9625] 11797 11895 11993 12091 12189 12287 12385 12483 12581 12679 12777 12875
##  [9637] 12973 13071 13169 13267 13365 13463 13561 13659 13757 13855 13953 14051
##  [9649] 14149 14247 14345 14443 14541 14639 14737 14835 14933 15031 15129 15227
##  [9661] 15325 15423 15521 15619 15717 15815 15913 16011 16109 16207 16305 16403
##  [9673] 16501 16599 16697 16795 16893 16991 17089 17187 17285 17383 17481 17579
##  [9685] 17677 17775 17873 17971 18069 18167 18265 18363 18461 18559 18657 18755
##  [9697] 18853 18951 19049 19147 19245 19343 19441 19539 19637 19735 19833 19931
##  [9709] 20029 20127 20225 20323 20421 20519 20617 20715 20813 20911 21009 21107
##  [9721] 21205 21303 21401 21499 21597 21695 21793 21891 21989 22087 22185 22283
##  [9733] 22381 22479 22577 22675 22773 22871 22969 23067 23165 23263 23361 23459
##  [9745] 23557 23655 23753 23851 23949 24047 24145 24243 24341 24439 24537 24635
##  [9757] 24733 24831 24929 25027 25125 25223 25321 25419 25517 25615 25713 25811
##  [9769]    38   136   234   332   430   528   626   724   822   920  1018  1116
##  [9781]  1214  1312  1410  1508  1606  1704  1802  1900  1998  2096  2194  2292
##  [9793]  2390  2488  2586  2684  2782  2880  2978  3076  3174  3272  3370  3468
##  [9805]  3566  3664  3762  3860  3958  4056  4154  4252  4350  4448  4546  4644
##  [9817]  4742  4840  4938  5036  5134  5232  5330  5428  5526  5624  5722  5820
##  [9829]  5918  6016  6114  6212  6310  6408  6506  6604  6702  6800  6898  6996
##  [9841]  7094  7192  7290  7388  7486  7584  7682  7780  7878  7976  8074  8172
##  [9853]  8270  8368  8466  8564  8662  8760  8858  8956  9054  9152  9250  9348
##  [9865]  9446  9544  9642  9740  9838  9936 10034 10132 10230 10328 10426 10524
##  [9877] 10622 10720 10818 10916 11014 11112 11210 11308 11406 11504 11602 11700
##  [9889] 11798 11896 11994 12092 12190 12288 12386 12484 12582 12680 12778 12876
##  [9901] 12974 13072 13170 13268 13366 13464 13562 13660 13758 13856 13954 14052
##  [9913] 14150 14248 14346 14444 14542 14640 14738 14836 14934 15032 15130 15228
##  [9925] 15326 15424 15522 15620 15718 15816 15914 16012 16110 16208 16306 16404
##  [9937] 16502 16600 16698 16796 16894 16992 17090 17188 17286 17384 17482 17580
##  [9949] 17678 17776 17874 17972 18070 18168 18266 18364 18462 18560 18658 18756
##  [9961] 18854 18952 19050 19148 19246 19344 19442 19540 19638 19736 19834 19932
##  [9973] 20030 20128 20226 20324 20422 20520 20618 20716 20814 20912 21010 21108
##  [9985] 21206 21304 21402 21500 21598 21696 21794 21892 21990 22088 22186 22284
##  [9997] 22382 22480 22578 22676 22774 22872 22970 23068 23166 23264 23362 23460
## [10009] 23558 23656 23754 23852 23950 24048 24146 24244 24342 24440 24538 24636
## [10021] 24734 24832 24930 25028 25126 25224 25322 25420 25518 25616 25714 25812
## [10033]    39   137   235   333   431   529   627   725   823   921  1019  1117
## [10045]  1215  1313  1411  1509  1607  1705  1803  1901  1999  2097  2195  2293
## [10057]  2391  2489  2587  2685  2783  2881  2979  3077  3175  3273  3371  3469
## [10069]  3567  3665  3763  3861  3959  4057  4155  4253  4351  4449  4547  4645
## [10081]  4743  4841  4939  5037  5135  5233  5331  5429  5527  5625  5723  5821
## [10093]  5919  6017  6115  6213  6311  6409  6507  6605  6703  6801  6899  6997
## [10105]  7095  7193  7291  7389  7487  7585  7683  7781  7879  7977  8075  8173
## [10117]  8271  8369  8467  8565  8663  8761  8859  8957  9055  9153  9251  9349
## [10129]  9447  9545  9643  9741  9839  9937 10035 10133 10231 10329 10427 10525
## [10141] 10623 10721 10819 10917 11015 11113 11211 11309 11407 11505 11603 11701
## [10153] 11799 11897 11995 12093 12191 12289 12387 12485 12583 12681 12779 12877
## [10165] 12975 13073 13171 13269 13367 13465 13563 13661 13759 13857 13955 14053
## [10177] 14151 14249 14347 14445 14543 14641 14739 14837 14935 15033 15131 15229
## [10189] 15327 15425 15523 15621 15719 15817 15915 16013 16111 16209 16307 16405
## [10201] 16503 16601 16699 16797 16895 16993 17091 17189 17287 17385 17483 17581
## [10213] 17679 17777 17875 17973 18071 18169 18267 18365 18463 18561 18659 18757
## [10225] 18855 18953 19051 19149 19247 19345 19443 19541 19639 19737 19835 19933
## [10237] 20031 20129 20227 20325 20423 20521 20619 20717 20815 20913 21011 21109
## [10249] 21207 21305 21403 21501 21599 21697 21795 21893 21991 22089 22187 22285
## [10261] 22383 22481 22579 22677 22775 22873 22971 23069 23167 23265 23363 23461
## [10273] 23559 23657 23755 23853 23951 24049 24147 24245 24343 24441 24539 24637
## [10285] 24735 24833 24931 25029 25127 25225 25323 25421 25519 25617 25715 25813
## [10297]    40   138   236   334   432   530   628   726   824   922  1020  1118
## [10309]  1216  1314  1412  1510  1608  1706  1804  1902  2000  2098  2196  2294
## [10321]  2392  2490  2588  2686  2784  2882  2980  3078  3176  3274  3372  3470
## [10333]  3568  3666  3764  3862  3960  4058  4156  4254  4352  4450  4548  4646
## [10345]  4744  4842  4940  5038  5136  5234  5332  5430  5528  5626  5724  5822
## [10357]  5920  6018  6116  6214  6312  6410  6508  6606  6704  6802  6900  6998
## [10369]  7096  7194  7292  7390  7488  7586  7684  7782  7880  7978  8076  8174
## [10381]  8272  8370  8468  8566  8664  8762  8860  8958  9056  9154  9252  9350
## [10393]  9448  9546  9644  9742  9840  9938 10036 10134 10232 10330 10428 10526
## [10405] 10624 10722 10820 10918 11016 11114 11212 11310 11408 11506 11604 11702
## [10417] 11800 11898 11996 12094 12192 12290 12388 12486 12584 12682 12780 12878
## [10429] 12976 13074 13172 13270 13368 13466 13564 13662 13760 13858 13956 14054
## [10441] 14152 14250 14348 14446 14544 14642 14740 14838 14936 15034 15132 15230
## [10453] 15328 15426 15524 15622 15720 15818 15916 16014 16112 16210 16308 16406
## [10465] 16504 16602 16700 16798 16896 16994 17092 17190 17288 17386 17484 17582
## [10477] 17680 17778 17876 17974 18072 18170 18268 18366 18464 18562 18660 18758
## [10489] 18856 18954 19052 19150 19248 19346 19444 19542 19640 19738 19836 19934
## [10501] 20032 20130 20228 20326 20424 20522 20620 20718 20816 20914 21012 21110
## [10513] 21208 21306 21404 21502 21600 21698 21796 21894 21992 22090 22188 22286
## [10525] 22384 22482 22580 22678 22776 22874 22972 23070 23168 23266 23364 23462
## [10537] 23560 23658 23756 23854 23952 24050 24148 24246 24344 24442 24540 24638
## [10549] 24736 24834 24932 25030 25128 25226 25324 25422 25520 25618 25716 25814
## [10561]    41   139   237   335   433   531   629   727   825   923  1021  1119
## [10573]  1217  1315  1413  1511  1609  1707  1805  1903  2001  2099  2197  2295
## [10585]  2393  2491  2589  2687  2785  2883  2981  3079  3177  3275  3373  3471
## [10597]  3569  3667  3765  3863  3961  4059  4157  4255  4353  4451  4549  4647
## [10609]  4745  4843  4941  5039  5137  5235  5333  5431  5529  5627  5725  5823
## [10621]  5921  6019  6117  6215  6313  6411  6509  6607  6705  6803  6901  6999
## [10633]  7097  7195  7293  7391  7489  7587  7685  7783  7881  7979  8077  8175
## [10645]  8273  8371  8469  8567  8665  8763  8861  8959  9057  9155  9253  9351
## [10657]  9449  9547  9645  9743  9841  9939 10037 10135 10233 10331 10429 10527
## [10669] 10625 10723 10821 10919 11017 11115 11213 11311 11409 11507 11605 11703
## [10681] 11801 11899 11997 12095 12193 12291 12389 12487 12585 12683 12781 12879
## [10693] 12977 13075 13173 13271 13369 13467 13565 13663 13761 13859 13957 14055
## [10705] 14153 14251 14349 14447 14545 14643 14741 14839 14937 15035 15133 15231
## [10717] 15329 15427 15525 15623 15721 15819 15917 16015 16113 16211 16309 16407
## [10729] 16505 16603 16701 16799 16897 16995 17093 17191 17289 17387 17485 17583
## [10741] 17681 17779 17877 17975 18073 18171 18269 18367 18465 18563 18661 18759
## [10753] 18857 18955 19053 19151 19249 19347 19445 19543 19641 19739 19837 19935
## [10765] 20033 20131 20229 20327 20425 20523 20621 20719 20817 20915 21013 21111
## [10777] 21209 21307 21405 21503 21601 21699 21797 21895 21993 22091 22189 22287
## [10789] 22385 22483 22581 22679 22777 22875 22973 23071 23169 23267 23365 23463
## [10801] 23561 23659 23757 23855 23953 24051 24149 24247 24345 24443 24541 24639
## [10813] 24737 24835 24933 25031 25129 25227 25325 25423 25521 25619 25717 25815
## [10825]    42   140   238   336   434   532   630   728   826   924  1022  1120
## [10837]  1218  1316  1414  1512  1610  1708  1806  1904  2002  2100  2198  2296
## [10849]  2394  2492  2590  2688  2786  2884  2982  3080  3178  3276  3374  3472
## [10861]  3570  3668  3766  3864  3962  4060  4158  4256  4354  4452  4550  4648
## [10873]  4746  4844  4942  5040  5138  5236  5334  5432  5530  5628  5726  5824
## [10885]  5922  6020  6118  6216  6314  6412  6510  6608  6706  6804  6902  7000
## [10897]  7098  7196  7294  7392  7490  7588  7686  7784  7882  7980  8078  8176
## [10909]  8274  8372  8470  8568  8666  8764  8862  8960  9058  9156  9254  9352
## [10921]  9450  9548  9646  9744  9842  9940 10038 10136 10234 10332 10430 10528
## [10933] 10626 10724 10822 10920 11018 11116 11214 11312 11410 11508 11606 11704
## [10945] 11802 11900 11998 12096 12194 12292 12390 12488 12586 12684 12782 12880
## [10957] 12978 13076 13174 13272 13370 13468 13566 13664 13762 13860 13958 14056
## [10969] 14154 14252 14350 14448 14546 14644 14742 14840 14938 15036 15134 15232
## [10981] 15330 15428 15526 15624 15722 15820 15918 16016 16114 16212 16310 16408
## [10993] 16506 16604 16702 16800 16898 16996 17094 17192 17290 17388 17486 17584
## [11005] 17682 17780 17878 17976 18074 18172 18270 18368 18466 18564 18662 18760
## [11017] 18858 18956 19054 19152 19250 19348 19446 19544 19642 19740 19838 19936
## [11029] 20034 20132 20230 20328 20426 20524 20622 20720 20818 20916 21014 21112
## [11041] 21210 21308 21406 21504 21602 21700 21798 21896 21994 22092 22190 22288
## [11053] 22386 22484 22582 22680 22778 22876 22974 23072 23170 23268 23366 23464
## [11065] 23562 23660 23758 23856 23954 24052 24150 24248 24346 24444 24542 24640
## [11077] 24738 24836 24934 25032 25130 25228 25326 25424 25522 25620 25718 25816
## [11089]    43   141   239   337   435   533   631   729   827   925  1023  1121
## [11101]  1219  1317  1415  1513  1611  1709  1807  1905  2003  2101  2199  2297
## [11113]  2395  2493  2591  2689  2787  2885  2983  3081  3179  3277  3375  3473
## [11125]  3571  3669  3767  3865  3963  4061  4159  4257  4355  4453  4551  4649
## [11137]  4747  4845  4943  5041  5139  5237  5335  5433  5531  5629  5727  5825
## [11149]  5923  6021  6119  6217  6315  6413  6511  6609  6707  6805  6903  7001
## [11161]  7099  7197  7295  7393  7491  7589  7687  7785  7883  7981  8079  8177
## [11173]  8275  8373  8471  8569  8667  8765  8863  8961  9059  9157  9255  9353
## [11185]  9451  9549  9647  9745  9843  9941 10039 10137 10235 10333 10431 10529
## [11197] 10627 10725 10823 10921 11019 11117 11215 11313 11411 11509 11607 11705
## [11209] 11803 11901 11999 12097 12195 12293 12391 12489 12587 12685 12783 12881
## [11221] 12979 13077 13175 13273 13371 13469 13567 13665 13763 13861 13959 14057
## [11233] 14155 14253 14351 14449 14547 14645 14743 14841 14939 15037 15135 15233
## [11245] 15331 15429 15527 15625 15723 15821 15919 16017 16115 16213 16311 16409
## [11257] 16507 16605 16703 16801 16899 16997 17095 17193 17291 17389 17487 17585
## [11269] 17683 17781 17879 17977 18075 18173 18271 18369 18467 18565 18663 18761
## [11281] 18859 18957 19055 19153 19251 19349 19447 19545 19643 19741 19839 19937
## [11293] 20035 20133 20231 20329 20427 20525 20623 20721 20819 20917 21015 21113
## [11305] 21211 21309 21407 21505 21603 21701 21799 21897 21995 22093 22191 22289
## [11317] 22387 22485 22583 22681 22779 22877 22975 23073 23171 23269 23367 23465
## [11329] 23563 23661 23759 23857 23955 24053 24151 24249 24347 24445 24543 24641
## [11341] 24739 24837 24935 25033 25131 25229 25327 25425 25523 25621 25719 25817
## [11353]    44   142   240   338   436   534   632   730   828   926  1024  1122
## [11365]  1220  1318  1416  1514  1612  1710  1808  1906  2004  2102  2200  2298
## [11377]  2396  2494  2592  2690  2788  2886  2984  3082  3180  3278  3376  3474
## [11389]  3572  3670  3768  3866  3964  4062  4160  4258  4356  4454  4552  4650
## [11401]  4748  4846  4944  5042  5140  5238  5336  5434  5532  5630  5728  5826
## [11413]  5924  6022  6120  6218  6316  6414  6512  6610  6708  6806  6904  7002
## [11425]  7100  7198  7296  7394  7492  7590  7688  7786  7884  7982  8080  8178
## [11437]  8276  8374  8472  8570  8668  8766  8864  8962  9060  9158  9256  9354
## [11449]  9452  9550  9648  9746  9844  9942 10040 10138 10236 10334 10432 10530
## [11461] 10628 10726 10824 10922 11020 11118 11216 11314 11412 11510 11608 11706
## [11473] 11804 11902 12000 12098 12196 12294 12392 12490 12588 12686 12784 12882
## [11485] 12980 13078 13176 13274 13372 13470 13568 13666 13764 13862 13960 14058
## [11497] 14156 14254 14352 14450 14548 14646 14744 14842 14940 15038 15136 15234
## [11509] 15332 15430 15528 15626 15724 15822 15920 16018 16116 16214 16312 16410
## [11521] 16508 16606 16704 16802 16900 16998 17096 17194 17292 17390 17488 17586
## [11533] 17684 17782 17880 17978 18076 18174 18272 18370 18468 18566 18664 18762
## [11545] 18860 18958 19056 19154 19252 19350 19448 19546 19644 19742 19840 19938
## [11557] 20036 20134 20232 20330 20428 20526 20624 20722 20820 20918 21016 21114
## [11569] 21212 21310 21408 21506 21604 21702 21800 21898 21996 22094 22192 22290
## [11581] 22388 22486 22584 22682 22780 22878 22976 23074 23172 23270 23368 23466
## [11593] 23564 23662 23760 23858 23956 24054 24152 24250 24348 24446 24544 24642
## [11605] 24740 24838 24936 25034 25132 25230 25328 25426 25524 25622 25720 25818
## [11617]    45   143   241   339   437   535   633   731   829   927  1025  1123
## [11629]  1221  1319  1417  1515  1613  1711  1809  1907  2005  2103  2201  2299
## [11641]  2397  2495  2593  2691  2789  2887  2985  3083  3181  3279  3377  3475
## [11653]  3573  3671  3769  3867  3965  4063  4161  4259  4357  4455  4553  4651
## [11665]  4749  4847  4945  5043  5141  5239  5337  5435  5533  5631  5729  5827
## [11677]  5925  6023  6121  6219  6317  6415  6513  6611  6709  6807  6905  7003
## [11689]  7101  7199  7297  7395  7493  7591  7689  7787  7885  7983  8081  8179
## [11701]  8277  8375  8473  8571  8669  8767  8865  8963  9061  9159  9257  9355
## [11713]  9453  9551  9649  9747  9845  9943 10041 10139 10237 10335 10433 10531
## [11725] 10629 10727 10825 10923 11021 11119 11217 11315 11413 11511 11609 11707
## [11737] 11805 11903 12001 12099 12197 12295 12393 12491 12589 12687 12785 12883
## [11749] 12981 13079 13177 13275 13373 13471 13569 13667 13765 13863 13961 14059
## [11761] 14157 14255 14353 14451 14549 14647 14745 14843 14941 15039 15137 15235
## [11773] 15333 15431 15529 15627 15725 15823 15921 16019 16117 16215 16313 16411
## [11785] 16509 16607 16705 16803 16901 16999 17097 17195 17293 17391 17489 17587
## [11797] 17685 17783 17881 17979 18077 18175 18273 18371 18469 18567 18665 18763
## [11809] 18861 18959 19057 19155 19253 19351 19449 19547 19645 19743 19841 19939
## [11821] 20037 20135 20233 20331 20429 20527 20625 20723 20821 20919 21017 21115
## [11833] 21213 21311 21409 21507 21605 21703 21801 21899 21997 22095 22193 22291
## [11845] 22389 22487 22585 22683 22781 22879 22977 23075 23173 23271 23369 23467
## [11857] 23565 23663 23761 23859 23957 24055 24153 24251 24349 24447 24545 24643
## [11869] 24741 24839 24937 25035 25133 25231 25329 25427 25525 25623 25721 25819
## [11881]    46   144   242   340   438   536   634   732   830   928  1026  1124
## [11893]  1222  1320  1418  1516  1614  1712  1810  1908  2006  2104  2202  2300
## [11905]  2398  2496  2594  2692  2790  2888  2986  3084  3182  3280  3378  3476
## [11917]  3574  3672  3770  3868  3966  4064  4162  4260  4358  4456  4554  4652
## [11929]  4750  4848  4946  5044  5142  5240  5338  5436  5534  5632  5730  5828
## [11941]  5926  6024  6122  6220  6318  6416  6514  6612  6710  6808  6906  7004
## [11953]  7102  7200  7298  7396  7494  7592  7690  7788  7886  7984  8082  8180
## [11965]  8278  8376  8474  8572  8670  8768  8866  8964  9062  9160  9258  9356
## [11977]  9454  9552  9650  9748  9846  9944 10042 10140 10238 10336 10434 10532
## [11989] 10630 10728 10826 10924 11022 11120 11218 11316 11414 11512 11610 11708
## [12001] 11806 11904 12002 12100 12198 12296 12394 12492 12590 12688 12786 12884
## [12013] 12982 13080 13178 13276 13374 13472 13570 13668 13766 13864 13962 14060
## [12025] 14158 14256 14354 14452 14550 14648 14746 14844 14942 15040 15138 15236
## [12037] 15334 15432 15530 15628 15726 15824 15922 16020 16118 16216 16314 16412
## [12049] 16510 16608 16706 16804 16902 17000 17098 17196 17294 17392 17490 17588
## [12061] 17686 17784 17882 17980 18078 18176 18274 18372 18470 18568 18666 18764
## [12073] 18862 18960 19058 19156 19254 19352 19450 19548 19646 19744 19842 19940
## [12085] 20038 20136 20234 20332 20430 20528 20626 20724 20822 20920 21018 21116
## [12097] 21214 21312 21410 21508 21606 21704 21802 21900 21998 22096 22194 22292
## [12109] 22390 22488 22586 22684 22782 22880 22978 23076 23174 23272 23370 23468
## [12121] 23566 23664 23762 23860 23958 24056 24154 24252 24350 24448 24546 24644
## [12133] 24742 24840 24938 25036 25134 25232 25330 25428 25526 25624 25722 25820
## [12145]    47   145   243   341   439   537   635   733   831   929  1027  1125
## [12157]  1223  1321  1419  1517  1615  1713  1811  1909  2007  2105  2203  2301
## [12169]  2399  2497  2595  2693  2791  2889  2987  3085  3183  3281  3379  3477
## [12181]  3575  3673  3771  3869  3967  4065  4163  4261  4359  4457  4555  4653
## [12193]  4751  4849  4947  5045  5143  5241  5339  5437  5535  5633  5731  5829
## [12205]  5927  6025  6123  6221  6319  6417  6515  6613  6711  6809  6907  7005
## [12217]  7103  7201  7299  7397  7495  7593  7691  7789  7887  7985  8083  8181
## [12229]  8279  8377  8475  8573  8671  8769  8867  8965  9063  9161  9259  9357
## [12241]  9455  9553  9651  9749  9847  9945 10043 10141 10239 10337 10435 10533
## [12253] 10631 10729 10827 10925 11023 11121 11219 11317 11415 11513 11611 11709
## [12265] 11807 11905 12003 12101 12199 12297 12395 12493 12591 12689 12787 12885
## [12277] 12983 13081 13179 13277 13375 13473 13571 13669 13767 13865 13963 14061
## [12289] 14159 14257 14355 14453 14551 14649 14747 14845 14943 15041 15139 15237
## [12301] 15335 15433 15531 15629 15727 15825 15923 16021 16119 16217 16315 16413
## [12313] 16511 16609 16707 16805 16903 17001 17099 17197 17295 17393 17491 17589
## [12325] 17687 17785 17883 17981 18079 18177 18275 18373 18471 18569 18667 18765
## [12337] 18863 18961 19059 19157 19255 19353 19451 19549 19647 19745 19843 19941
## [12349] 20039 20137 20235 20333 20431 20529 20627 20725 20823 20921 21019 21117
## [12361] 21215 21313 21411 21509 21607 21705 21803 21901 21999 22097 22195 22293
## [12373] 22391 22489 22587 22685 22783 22881 22979 23077 23175 23273 23371 23469
## [12385] 23567 23665 23763 23861 23959 24057 24155 24253 24351 24449 24547 24645
## [12397] 24743 24841 24939 25037 25135 25233 25331 25429 25527 25625 25723 25821
## [12409]    48   146   244   342   440   538   636   734   832   930  1028  1126
## [12421]  1224  1322  1420  1518  1616  1714  1812  1910  2008  2106  2204  2302
## [12433]  2400  2498  2596  2694  2792  2890  2988  3086  3184  3282  3380  3478
## [12445]  3576  3674  3772  3870  3968  4066  4164  4262  4360  4458  4556  4654
## [12457]  4752  4850  4948  5046  5144  5242  5340  5438  5536  5634  5732  5830
## [12469]  5928  6026  6124  6222  6320  6418  6516  6614  6712  6810  6908  7006
## [12481]  7104  7202  7300  7398  7496  7594  7692  7790  7888  7986  8084  8182
## [12493]  8280  8378  8476  8574  8672  8770  8868  8966  9064  9162  9260  9358
## [12505]  9456  9554  9652  9750  9848  9946 10044 10142 10240 10338 10436 10534
## [12517] 10632 10730 10828 10926 11024 11122 11220 11318 11416 11514 11612 11710
## [12529] 11808 11906 12004 12102 12200 12298 12396 12494 12592 12690 12788 12886
## [12541] 12984 13082 13180 13278 13376 13474 13572 13670 13768 13866 13964 14062
## [12553] 14160 14258 14356 14454 14552 14650 14748 14846 14944 15042 15140 15238
## [12565] 15336 15434 15532 15630 15728 15826 15924 16022 16120 16218 16316 16414
## [12577] 16512 16610 16708 16806 16904 17002 17100 17198 17296 17394 17492 17590
## [12589] 17688 17786 17884 17982 18080 18178 18276 18374 18472 18570 18668 18766
## [12601] 18864 18962 19060 19158 19256 19354 19452 19550 19648 19746 19844 19942
## [12613] 20040 20138 20236 20334 20432 20530 20628 20726 20824 20922 21020 21118
## [12625] 21216 21314 21412 21510 21608 21706 21804 21902 22000 22098 22196 22294
## [12637] 22392 22490 22588 22686 22784 22882 22980 23078 23176 23274 23372 23470
## [12649] 23568 23666 23764 23862 23960 24058 24156 24254 24352 24450 24548 24646
## [12661] 24744 24842 24940 25038 25136 25234 25332 25430 25528 25626 25724 25822
## [12673]    49   147   245   343   441   539   637   735   833   931  1029  1127
## [12685]  1225  1323  1421  1519  1617  1715  1813  1911  2009  2107  2205  2303
## [12697]  2401  2499  2597  2695  2793  2891  2989  3087  3185  3283  3381  3479
## [12709]  3577  3675  3773  3871  3969  4067  4165  4263  4361  4459  4557  4655
## [12721]  4753  4851  4949  5047  5145  5243  5341  5439  5537  5635  5733  5831
## [12733]  5929  6027  6125  6223  6321  6419  6517  6615  6713  6811  6909  7007
## [12745]  7105  7203  7301  7399  7497  7595  7693  7791  7889  7987  8085  8183
## [12757]  8281  8379  8477  8575  8673  8771  8869  8967  9065  9163  9261  9359
## [12769]  9457  9555  9653  9751  9849  9947 10045 10143 10241 10339 10437 10535
## [12781] 10633 10731 10829 10927 11025 11123 11221 11319 11417 11515 11613 11711
## [12793] 11809 11907 12005 12103 12201 12299 12397 12495 12593 12691 12789 12887
## [12805] 12985 13083 13181 13279 13377 13475 13573 13671 13769 13867 13965 14063
## [12817] 14161 14259 14357 14455 14553 14651 14749 14847 14945 15043 15141 15239
## [12829] 15337 15435 15533 15631 15729 15827 15925 16023 16121 16219 16317 16415
## [12841] 16513 16611 16709 16807 16905 17003 17101 17199 17297 17395 17493 17591
## [12853] 17689 17787 17885 17983 18081 18179 18277 18375 18473 18571 18669 18767
## [12865] 18865 18963 19061 19159 19257 19355 19453 19551 19649 19747 19845 19943
## [12877] 20041 20139 20237 20335 20433 20531 20629 20727 20825 20923 21021 21119
## [12889] 21217 21315 21413 21511 21609 21707 21805 21903 22001 22099 22197 22295
## [12901] 22393 22491 22589 22687 22785 22883 22981 23079 23177 23275 23373 23471
## [12913] 23569 23667 23765 23863 23961 24059 24157 24255 24353 24451 24549 24647
## [12925] 24745 24843 24941 25039 25137 25235 25333 25431 25529 25627 25725 25823
## [12937]    50   148   246   344   442   540   638   736   834   932  1030  1128
## [12949]  1226  1324  1422  1520  1618  1716  1814  1912  2010  2108  2206  2304
## [12961]  2402  2500  2598  2696  2794  2892  2990  3088  3186  3284  3382  3480
## [12973]  3578  3676  3774  3872  3970  4068  4166  4264  4362  4460  4558  4656
## [12985]  4754  4852  4950  5048  5146  5244  5342  5440  5538  5636  5734  5832
## [12997]  5930  6028  6126  6224  6322  6420  6518  6616  6714  6812  6910  7008
## [13009]  7106  7204  7302  7400  7498  7596  7694  7792  7890  7988  8086  8184
## [13021]  8282  8380  8478  8576  8674  8772  8870  8968  9066  9164  9262  9360
## [13033]  9458  9556  9654  9752  9850  9948 10046 10144 10242 10340 10438 10536
## [13045] 10634 10732 10830 10928 11026 11124 11222 11320 11418 11516 11614 11712
## [13057] 11810 11908 12006 12104 12202 12300 12398 12496 12594 12692 12790 12888
## [13069] 12986 13084 13182 13280 13378 13476 13574 13672 13770 13868 13966 14064
## [13081] 14162 14260 14358 14456 14554 14652 14750 14848 14946 15044 15142 15240
## [13093] 15338 15436 15534 15632 15730 15828 15926 16024 16122 16220 16318 16416
## [13105] 16514 16612 16710 16808 16906 17004 17102 17200 17298 17396 17494 17592
## [13117] 17690 17788 17886 17984 18082 18180 18278 18376 18474 18572 18670 18768
## [13129] 18866 18964 19062 19160 19258 19356 19454 19552 19650 19748 19846 19944
## [13141] 20042 20140 20238 20336 20434 20532 20630 20728 20826 20924 21022 21120
## [13153] 21218 21316 21414 21512 21610 21708 21806 21904 22002 22100 22198 22296
## [13165] 22394 22492 22590 22688 22786 22884 22982 23080 23178 23276 23374 23472
## [13177] 23570 23668 23766 23864 23962 24060 24158 24256 24354 24452 24550 24648
## [13189] 24746 24844 24942 25040 25138 25236 25334 25432 25530 25628 25726 25824
## [13201]    51   149   247   345   443   541   639   737   835   933  1031  1129
## [13213]  1227  1325  1423  1521  1619  1717  1815  1913  2011  2109  2207  2305
## [13225]  2403  2501  2599  2697  2795  2893  2991  3089  3187  3285  3383  3481
## [13237]  3579  3677  3775  3873  3971  4069  4167  4265  4363  4461  4559  4657
## [13249]  4755  4853  4951  5049  5147  5245  5343  5441  5539  5637  5735  5833
## [13261]  5931  6029  6127  6225  6323  6421  6519  6617  6715  6813  6911  7009
## [13273]  7107  7205  7303  7401  7499  7597  7695  7793  7891  7989  8087  8185
## [13285]  8283  8381  8479  8577  8675  8773  8871  8969  9067  9165  9263  9361
## [13297]  9459  9557  9655  9753  9851  9949 10047 10145 10243 10341 10439 10537
## [13309] 10635 10733 10831 10929 11027 11125 11223 11321 11419 11517 11615 11713
## [13321] 11811 11909 12007 12105 12203 12301 12399 12497 12595 12693 12791 12889
## [13333] 12987 13085 13183 13281 13379 13477 13575 13673 13771 13869 13967 14065
## [13345] 14163 14261 14359 14457 14555 14653 14751 14849 14947 15045 15143 15241
## [13357] 15339 15437 15535 15633 15731 15829 15927 16025 16123 16221 16319 16417
## [13369] 16515 16613 16711 16809 16907 17005 17103 17201 17299 17397 17495 17593
## [13381] 17691 17789 17887 17985 18083 18181 18279 18377 18475 18573 18671 18769
## [13393] 18867 18965 19063 19161 19259 19357 19455 19553 19651 19749 19847 19945
## [13405] 20043 20141 20239 20337 20435 20533 20631 20729 20827 20925 21023 21121
## [13417] 21219 21317 21415 21513 21611 21709 21807 21905 22003 22101 22199 22297
## [13429] 22395 22493 22591 22689 22787 22885 22983 23081 23179 23277 23375 23473
## [13441] 23571 23669 23767 23865 23963 24061 24159 24257 24355 24453 24551 24649
## [13453] 24747 24845 24943 25041 25139 25237 25335 25433 25531 25629 25727 25825
## [13465]    52   150   248   346   444   542   640   738   836   934  1032  1130
## [13477]  1228  1326  1424  1522  1620  1718  1816  1914  2012  2110  2208  2306
## [13489]  2404  2502  2600  2698  2796  2894  2992  3090  3188  3286  3384  3482
## [13501]  3580  3678  3776  3874  3972  4070  4168  4266  4364  4462  4560  4658
## [13513]  4756  4854  4952  5050  5148  5246  5344  5442  5540  5638  5736  5834
## [13525]  5932  6030  6128  6226  6324  6422  6520  6618  6716  6814  6912  7010
## [13537]  7108  7206  7304  7402  7500  7598  7696  7794  7892  7990  8088  8186
## [13549]  8284  8382  8480  8578  8676  8774  8872  8970  9068  9166  9264  9362
## [13561]  9460  9558  9656  9754  9852  9950 10048 10146 10244 10342 10440 10538
## [13573] 10636 10734 10832 10930 11028 11126 11224 11322 11420 11518 11616 11714
## [13585] 11812 11910 12008 12106 12204 12302 12400 12498 12596 12694 12792 12890
## [13597] 12988 13086 13184 13282 13380 13478 13576 13674 13772 13870 13968 14066
## [13609] 14164 14262 14360 14458 14556 14654 14752 14850 14948 15046 15144 15242
## [13621] 15340 15438 15536 15634 15732 15830 15928 16026 16124 16222 16320 16418
## [13633] 16516 16614 16712 16810 16908 17006 17104 17202 17300 17398 17496 17594
## [13645] 17692 17790 17888 17986 18084 18182 18280 18378 18476 18574 18672 18770
## [13657] 18868 18966 19064 19162 19260 19358 19456 19554 19652 19750 19848 19946
## [13669] 20044 20142 20240 20338 20436 20534 20632 20730 20828 20926 21024 21122
## [13681] 21220 21318 21416 21514 21612 21710 21808 21906 22004 22102 22200 22298
## [13693] 22396 22494 22592 22690 22788 22886 22984 23082 23180 23278 23376 23474
## [13705] 23572 23670 23768 23866 23964 24062 24160 24258 24356 24454 24552 24650
## [13717] 24748 24846 24944 25042 25140 25238 25336 25434 25532 25630 25728 25826
## [13729]    53   151   249   347   445   543   641   739   837   935  1033  1131
## [13741]  1229  1327  1425  1523  1621  1719  1817  1915  2013  2111  2209  2307
## [13753]  2405  2503  2601  2699  2797  2895  2993  3091  3189  3287  3385  3483
## [13765]  3581  3679  3777  3875  3973  4071  4169  4267  4365  4463  4561  4659
## [13777]  4757  4855  4953  5051  5149  5247  5345  5443  5541  5639  5737  5835
## [13789]  5933  6031  6129  6227  6325  6423  6521  6619  6717  6815  6913  7011
## [13801]  7109  7207  7305  7403  7501  7599  7697  7795  7893  7991  8089  8187
## [13813]  8285  8383  8481  8579  8677  8775  8873  8971  9069  9167  9265  9363
## [13825]  9461  9559  9657  9755  9853  9951 10049 10147 10245 10343 10441 10539
## [13837] 10637 10735 10833 10931 11029 11127 11225 11323 11421 11519 11617 11715
## [13849] 11813 11911 12009 12107 12205 12303 12401 12499 12597 12695 12793 12891
## [13861] 12989 13087 13185 13283 13381 13479 13577 13675 13773 13871 13969 14067
## [13873] 14165 14263 14361 14459 14557 14655 14753 14851 14949 15047 15145 15243
## [13885] 15341 15439 15537 15635 15733 15831 15929 16027 16125 16223 16321 16419
## [13897] 16517 16615 16713 16811 16909 17007 17105 17203 17301 17399 17497 17595
## [13909] 17693 17791 17889 17987 18085 18183 18281 18379 18477 18575 18673 18771
## [13921] 18869 18967 19065 19163 19261 19359 19457 19555 19653 19751 19849 19947
## [13933] 20045 20143 20241 20339 20437 20535 20633 20731 20829 20927 21025 21123
## [13945] 21221 21319 21417 21515 21613 21711 21809 21907 22005 22103 22201 22299
## [13957] 22397 22495 22593 22691 22789 22887 22985 23083 23181 23279 23377 23475
## [13969] 23573 23671 23769 23867 23965 24063 24161 24259 24357 24455 24553 24651
## [13981] 24749 24847 24945 25043 25141 25239 25337 25435 25533 25631 25729 25827
## [13993]    54   152   250   348   446   544   642   740   838   936  1034  1132
## [14005]  1230  1328  1426  1524  1622  1720  1818  1916  2014  2112  2210  2308
## [14017]  2406  2504  2602  2700  2798  2896  2994  3092  3190  3288  3386  3484
## [14029]  3582  3680  3778  3876  3974  4072  4170  4268  4366  4464  4562  4660
## [14041]  4758  4856  4954  5052  5150  5248  5346  5444  5542  5640  5738  5836
## [14053]  5934  6032  6130  6228  6326  6424  6522  6620  6718  6816  6914  7012
## [14065]  7110  7208  7306  7404  7502  7600  7698  7796  7894  7992  8090  8188
## [14077]  8286  8384  8482  8580  8678  8776  8874  8972  9070  9168  9266  9364
## [14089]  9462  9560  9658  9756  9854  9952 10050 10148 10246 10344 10442 10540
## [14101] 10638 10736 10834 10932 11030 11128 11226 11324 11422 11520 11618 11716
## [14113] 11814 11912 12010 12108 12206 12304 12402 12500 12598 12696 12794 12892
## [14125] 12990 13088 13186 13284 13382 13480 13578 13676 13774 13872 13970 14068
## [14137] 14166 14264 14362 14460 14558 14656 14754 14852 14950 15048 15146 15244
## [14149] 15342 15440 15538 15636 15734 15832 15930 16028 16126 16224 16322 16420
## [14161] 16518 16616 16714 16812 16910 17008 17106 17204 17302 17400 17498 17596
## [14173] 17694 17792 17890 17988 18086 18184 18282 18380 18478 18576 18674 18772
## [14185] 18870 18968 19066 19164 19262 19360 19458 19556 19654 19752 19850 19948
## [14197] 20046 20144 20242 20340 20438 20536 20634 20732 20830 20928 21026 21124
## [14209] 21222 21320 21418 21516 21614 21712 21810 21908 22006 22104 22202 22300
## [14221] 22398 22496 22594 22692 22790 22888 22986 23084 23182 23280 23378 23476
## [14233] 23574 23672 23770 23868 23966 24064 24162 24260 24358 24456 24554 24652
## [14245] 24750 24848 24946 25044 25142 25240 25338 25436 25534 25632 25730 25828
## [14257]    55   153   251   349   447   545   643   741   839   937  1035  1133
## [14269]  1231  1329  1427  1525  1623  1721  1819  1917  2015  2113  2211  2309
## [14281]  2407  2505  2603  2701  2799  2897  2995  3093  3191  3289  3387  3485
## [14293]  3583  3681  3779  3877  3975  4073  4171  4269  4367  4465  4563  4661
## [14305]  4759  4857  4955  5053  5151  5249  5347  5445  5543  5641  5739  5837
## [14317]  5935  6033  6131  6229  6327  6425  6523  6621  6719  6817  6915  7013
## [14329]  7111  7209  7307  7405  7503  7601  7699  7797  7895  7993  8091  8189
## [14341]  8287  8385  8483  8581  8679  8777  8875  8973  9071  9169  9267  9365
## [14353]  9463  9561  9659  9757  9855  9953 10051 10149 10247 10345 10443 10541
## [14365] 10639 10737 10835 10933 11031 11129 11227 11325 11423 11521 11619 11717
## [14377] 11815 11913 12011 12109 12207 12305 12403 12501 12599 12697 12795 12893
## [14389] 12991 13089 13187 13285 13383 13481 13579 13677 13775 13873 13971 14069
## [14401] 14167 14265 14363 14461 14559 14657 14755 14853 14951 15049 15147 15245
## [14413] 15343 15441 15539 15637 15735 15833 15931 16029 16127 16225 16323 16421
## [14425] 16519 16617 16715 16813 16911 17009 17107 17205 17303 17401 17499 17597
## [14437] 17695 17793 17891 17989 18087 18185 18283 18381 18479 18577 18675 18773
## [14449] 18871 18969 19067 19165 19263 19361 19459 19557 19655 19753 19851 19949
## [14461] 20047 20145 20243 20341 20439 20537 20635 20733 20831 20929 21027 21125
## [14473] 21223 21321 21419 21517 21615 21713 21811 21909 22007 22105 22203 22301
## [14485] 22399 22497 22595 22693 22791 22889 22987 23085 23183 23281 23379 23477
## [14497] 23575 23673 23771 23869 23967 24065 24163 24261 24359 24457 24555 24653
## [14509] 24751 24849 24947 25045 25143 25241 25339 25437 25535 25633 25731 25829
## [14521]    56   154   252   350   448   546   644   742   840   938  1036  1134
## [14533]  1232  1330  1428  1526  1624  1722  1820  1918  2016  2114  2212  2310
## [14545]  2408  2506  2604  2702  2800  2898  2996  3094  3192  3290  3388  3486
## [14557]  3584  3682  3780  3878  3976  4074  4172  4270  4368  4466  4564  4662
## [14569]  4760  4858  4956  5054  5152  5250  5348  5446  5544  5642  5740  5838
## [14581]  5936  6034  6132  6230  6328  6426  6524  6622  6720  6818  6916  7014
## [14593]  7112  7210  7308  7406  7504  7602  7700  7798  7896  7994  8092  8190
## [14605]  8288  8386  8484  8582  8680  8778  8876  8974  9072  9170  9268  9366
## [14617]  9464  9562  9660  9758  9856  9954 10052 10150 10248 10346 10444 10542
## [14629] 10640 10738 10836 10934 11032 11130 11228 11326 11424 11522 11620 11718
## [14641] 11816 11914 12012 12110 12208 12306 12404 12502 12600 12698 12796 12894
## [14653] 12992 13090 13188 13286 13384 13482 13580 13678 13776 13874 13972 14070
## [14665] 14168 14266 14364 14462 14560 14658 14756 14854 14952 15050 15148 15246
## [14677] 15344 15442 15540 15638 15736 15834 15932 16030 16128 16226 16324 16422
## [14689] 16520 16618 16716 16814 16912 17010 17108 17206 17304 17402 17500 17598
## [14701] 17696 17794 17892 17990 18088 18186 18284 18382 18480 18578 18676 18774
## [14713] 18872 18970 19068 19166 19264 19362 19460 19558 19656 19754 19852 19950
## [14725] 20048 20146 20244 20342 20440 20538 20636 20734 20832 20930 21028 21126
## [14737] 21224 21322 21420 21518 21616 21714 21812 21910 22008 22106 22204 22302
## [14749] 22400 22498 22596 22694 22792 22890 22988 23086 23184 23282 23380 23478
## [14761] 23576 23674 23772 23870 23968 24066 24164 24262 24360 24458 24556 24654
## [14773] 24752 24850 24948 25046 25144 25242 25340 25438 25536 25634 25732 25830
## [14785]    57   155   253   351   449   547   645   743   841   939  1037  1135
## [14797]  1233  1331  1429  1527  1625  1723  1821  1919  2017  2115  2213  2311
## [14809]  2409  2507  2605  2703  2801  2899  2997  3095  3193  3291  3389  3487
## [14821]  3585  3683  3781  3879  3977  4075  4173  4271  4369  4467  4565  4663
## [14833]  4761  4859  4957  5055  5153  5251  5349  5447  5545  5643  5741  5839
## [14845]  5937  6035  6133  6231  6329  6427  6525  6623  6721  6819  6917  7015
## [14857]  7113  7211  7309  7407  7505  7603  7701  7799  7897  7995  8093  8191
## [14869]  8289  8387  8485  8583  8681  8779  8877  8975  9073  9171  9269  9367
## [14881]  9465  9563  9661  9759  9857  9955 10053 10151 10249 10347 10445 10543
## [14893] 10641 10739 10837 10935 11033 11131 11229 11327 11425 11523 11621 11719
## [14905] 11817 11915 12013 12111 12209 12307 12405 12503 12601 12699 12797 12895
## [14917] 12993 13091 13189 13287 13385 13483 13581 13679 13777 13875 13973 14071
## [14929] 14169 14267 14365 14463 14561 14659 14757 14855 14953 15051 15149 15247
## [14941] 15345 15443 15541 15639 15737 15835 15933 16031 16129 16227 16325 16423
## [14953] 16521 16619 16717 16815 16913 17011 17109 17207 17305 17403 17501 17599
## [14965] 17697 17795 17893 17991 18089 18187 18285 18383 18481 18579 18677 18775
## [14977] 18873 18971 19069 19167 19265 19363 19461 19559 19657 19755 19853 19951
## [14989] 20049 20147 20245 20343 20441 20539 20637 20735 20833 20931 21029 21127
## [15001] 21225 21323 21421 21519 21617 21715 21813 21911 22009 22107 22205 22303
## [15013] 22401 22499 22597 22695 22793 22891 22989 23087 23185 23283 23381 23479
## [15025] 23577 23675 23773 23871 23969 24067 24165 24263 24361 24459 24557 24655
## [15037] 24753 24851 24949 25047 25145 25243 25341 25439 25537 25635 25733 25831
## [15049]    58   156   254   352   450   548   646   744   842   940  1038  1136
## [15061]  1234  1332  1430  1528  1626  1724  1822  1920  2018  2116  2214  2312
## [15073]  2410  2508  2606  2704  2802  2900  2998  3096  3194  3292  3390  3488
## [15085]  3586  3684  3782  3880  3978  4076  4174  4272  4370  4468  4566  4664
## [15097]  4762  4860  4958  5056  5154  5252  5350  5448  5546  5644  5742  5840
## [15109]  5938  6036  6134  6232  6330  6428  6526  6624  6722  6820  6918  7016
## [15121]  7114  7212  7310  7408  7506  7604  7702  7800  7898  7996  8094  8192
## [15133]  8290  8388  8486  8584  8682  8780  8878  8976  9074  9172  9270  9368
## [15145]  9466  9564  9662  9760  9858  9956 10054 10152 10250 10348 10446 10544
## [15157] 10642 10740 10838 10936 11034 11132 11230 11328 11426 11524 11622 11720
## [15169] 11818 11916 12014 12112 12210 12308 12406 12504 12602 12700 12798 12896
## [15181] 12994 13092 13190 13288 13386 13484 13582 13680 13778 13876 13974 14072
## [15193] 14170 14268 14366 14464 14562 14660 14758 14856 14954 15052 15150 15248
## [15205] 15346 15444 15542 15640 15738 15836 15934 16032 16130 16228 16326 16424
## [15217] 16522 16620 16718 16816 16914 17012 17110 17208 17306 17404 17502 17600
## [15229] 17698 17796 17894 17992 18090 18188 18286 18384 18482 18580 18678 18776
## [15241] 18874 18972 19070 19168 19266 19364 19462 19560 19658 19756 19854 19952
## [15253] 20050 20148 20246 20344 20442 20540 20638 20736 20834 20932 21030 21128
## [15265] 21226 21324 21422 21520 21618 21716 21814 21912 22010 22108 22206 22304
## [15277] 22402 22500 22598 22696 22794 22892 22990 23088 23186 23284 23382 23480
## [15289] 23578 23676 23774 23872 23970 24068 24166 24264 24362 24460 24558 24656
## [15301] 24754 24852 24950 25048 25146 25244 25342 25440 25538 25636 25734 25832
## [15313]    59   157   255   353   451   549   647   745   843   941  1039  1137
## [15325]  1235  1333  1431  1529  1627  1725  1823  1921  2019  2117  2215  2313
## [15337]  2411  2509  2607  2705  2803  2901  2999  3097  3195  3293  3391  3489
## [15349]  3587  3685  3783  3881  3979  4077  4175  4273  4371  4469  4567  4665
## [15361]  4763  4861  4959  5057  5155  5253  5351  5449  5547  5645  5743  5841
## [15373]  5939  6037  6135  6233  6331  6429  6527  6625  6723  6821  6919  7017
## [15385]  7115  7213  7311  7409  7507  7605  7703  7801  7899  7997  8095  8193
## [15397]  8291  8389  8487  8585  8683  8781  8879  8977  9075  9173  9271  9369
## [15409]  9467  9565  9663  9761  9859  9957 10055 10153 10251 10349 10447 10545
## [15421] 10643 10741 10839 10937 11035 11133 11231 11329 11427 11525 11623 11721
## [15433] 11819 11917 12015 12113 12211 12309 12407 12505 12603 12701 12799 12897
## [15445] 12995 13093 13191 13289 13387 13485 13583 13681 13779 13877 13975 14073
## [15457] 14171 14269 14367 14465 14563 14661 14759 14857 14955 15053 15151 15249
## [15469] 15347 15445 15543 15641 15739 15837 15935 16033 16131 16229 16327 16425
## [15481] 16523 16621 16719 16817 16915 17013 17111 17209 17307 17405 17503 17601
## [15493] 17699 17797 17895 17993 18091 18189 18287 18385 18483 18581 18679 18777
## [15505] 18875 18973 19071 19169 19267 19365 19463 19561 19659 19757 19855 19953
## [15517] 20051 20149 20247 20345 20443 20541 20639 20737 20835 20933 21031 21129
## [15529] 21227 21325 21423 21521 21619 21717 21815 21913 22011 22109 22207 22305
## [15541] 22403 22501 22599 22697 22795 22893 22991 23089 23187 23285 23383 23481
## [15553] 23579 23677 23775 23873 23971 24069 24167 24265 24363 24461 24559 24657
## [15565] 24755 24853 24951 25049 25147 25245 25343 25441 25539 25637 25735 25833
## [15577]    60   158   256   354   452   550   648   746   844   942  1040  1138
## [15589]  1236  1334  1432  1530  1628  1726  1824  1922  2020  2118  2216  2314
## [15601]  2412  2510  2608  2706  2804  2902  3000  3098  3196  3294  3392  3490
## [15613]  3588  3686  3784  3882  3980  4078  4176  4274  4372  4470  4568  4666
## [15625]  4764  4862  4960  5058  5156  5254  5352  5450  5548  5646  5744  5842
## [15637]  5940  6038  6136  6234  6332  6430  6528  6626  6724  6822  6920  7018
## [15649]  7116  7214  7312  7410  7508  7606  7704  7802  7900  7998  8096  8194
## [15661]  8292  8390  8488  8586  8684  8782  8880  8978  9076  9174  9272  9370
## [15673]  9468  9566  9664  9762  9860  9958 10056 10154 10252 10350 10448 10546
## [15685] 10644 10742 10840 10938 11036 11134 11232 11330 11428 11526 11624 11722
## [15697] 11820 11918 12016 12114 12212 12310 12408 12506 12604 12702 12800 12898
## [15709] 12996 13094 13192 13290 13388 13486 13584 13682 13780 13878 13976 14074
## [15721] 14172 14270 14368 14466 14564 14662 14760 14858 14956 15054 15152 15250
## [15733] 15348 15446 15544 15642 15740 15838 15936 16034 16132 16230 16328 16426
## [15745] 16524 16622 16720 16818 16916 17014 17112 17210 17308 17406 17504 17602
## [15757] 17700 17798 17896 17994 18092 18190 18288 18386 18484 18582 18680 18778
## [15769] 18876 18974 19072 19170 19268 19366 19464 19562 19660 19758 19856 19954
## [15781] 20052 20150 20248 20346 20444 20542 20640 20738 20836 20934 21032 21130
## [15793] 21228 21326 21424 21522 21620 21718 21816 21914 22012 22110 22208 22306
## [15805] 22404 22502 22600 22698 22796 22894 22992 23090 23188 23286 23384 23482
## [15817] 23580 23678 23776 23874 23972 24070 24168 24266 24364 24462 24560 24658
## [15829] 24756 24854 24952 25050 25148 25246 25344 25442 25540 25638 25736 25834
## [15841]    61   159   257   355   453   551   649   747   845   943  1041  1139
## [15853]  1237  1335  1433  1531  1629  1727  1825  1923  2021  2119  2217  2315
## [15865]  2413  2511  2609  2707  2805  2903  3001  3099  3197  3295  3393  3491
## [15877]  3589  3687  3785  3883  3981  4079  4177  4275  4373  4471  4569  4667
## [15889]  4765  4863  4961  5059  5157  5255  5353  5451  5549  5647  5745  5843
## [15901]  5941  6039  6137  6235  6333  6431  6529  6627  6725  6823  6921  7019
## [15913]  7117  7215  7313  7411  7509  7607  7705  7803  7901  7999  8097  8195
## [15925]  8293  8391  8489  8587  8685  8783  8881  8979  9077  9175  9273  9371
## [15937]  9469  9567  9665  9763  9861  9959 10057 10155 10253 10351 10449 10547
## [15949] 10645 10743 10841 10939 11037 11135 11233 11331 11429 11527 11625 11723
## [15961] 11821 11919 12017 12115 12213 12311 12409 12507 12605 12703 12801 12899
## [15973] 12997 13095 13193 13291 13389 13487 13585 13683 13781 13879 13977 14075
## [15985] 14173 14271 14369 14467 14565 14663 14761 14859 14957 15055 15153 15251
## [15997] 15349 15447 15545 15643 15741 15839 15937 16035 16133 16231 16329 16427
## [16009] 16525 16623 16721 16819 16917 17015 17113 17211 17309 17407 17505 17603
## [16021] 17701 17799 17897 17995 18093 18191 18289 18387 18485 18583 18681 18779
## [16033] 18877 18975 19073 19171 19269 19367 19465 19563 19661 19759 19857 19955
## [16045] 20053 20151 20249 20347 20445 20543 20641 20739 20837 20935 21033 21131
## [16057] 21229 21327 21425 21523 21621 21719 21817 21915 22013 22111 22209 22307
## [16069] 22405 22503 22601 22699 22797 22895 22993 23091 23189 23287 23385 23483
## [16081] 23581 23679 23777 23875 23973 24071 24169 24267 24365 24463 24561 24659
## [16093] 24757 24855 24953 25051 25149 25247 25345 25443 25541 25639 25737 25835
## [16105]    62   160   258   356   454   552   650   748   846   944  1042  1140
## [16117]  1238  1336  1434  1532  1630  1728  1826  1924  2022  2120  2218  2316
## [16129]  2414  2512  2610  2708  2806  2904  3002  3100  3198  3296  3394  3492
## [16141]  3590  3688  3786  3884  3982  4080  4178  4276  4374  4472  4570  4668
## [16153]  4766  4864  4962  5060  5158  5256  5354  5452  5550  5648  5746  5844
## [16165]  5942  6040  6138  6236  6334  6432  6530  6628  6726  6824  6922  7020
## [16177]  7118  7216  7314  7412  7510  7608  7706  7804  7902  8000  8098  8196
## [16189]  8294  8392  8490  8588  8686  8784  8882  8980  9078  9176  9274  9372
## [16201]  9470  9568  9666  9764  9862  9960 10058 10156 10254 10352 10450 10548
## [16213] 10646 10744 10842 10940 11038 11136 11234 11332 11430 11528 11626 11724
## [16225] 11822 11920 12018 12116 12214 12312 12410 12508 12606 12704 12802 12900
## [16237] 12998 13096 13194 13292 13390 13488 13586 13684 13782 13880 13978 14076
## [16249] 14174 14272 14370 14468 14566 14664 14762 14860 14958 15056 15154 15252
## [16261] 15350 15448 15546 15644 15742 15840 15938 16036 16134 16232 16330 16428
## [16273] 16526 16624 16722 16820 16918 17016 17114 17212 17310 17408 17506 17604
## [16285] 17702 17800 17898 17996 18094 18192 18290 18388 18486 18584 18682 18780
## [16297] 18878 18976 19074 19172 19270 19368 19466 19564 19662 19760 19858 19956
## [16309] 20054 20152 20250 20348 20446 20544 20642 20740 20838 20936 21034 21132
## [16321] 21230 21328 21426 21524 21622 21720 21818 21916 22014 22112 22210 22308
## [16333] 22406 22504 22602 22700 22798 22896 22994 23092 23190 23288 23386 23484
## [16345] 23582 23680 23778 23876 23974 24072 24170 24268 24366 24464 24562 24660
## [16357] 24758 24856 24954 25052 25150 25248 25346 25444 25542 25640 25738 25836
## [16369]    63   161   259   357   455   553   651   749   847   945  1043  1141
## [16381]  1239  1337  1435  1533  1631  1729  1827  1925  2023  2121  2219  2317
## [16393]  2415  2513  2611  2709  2807  2905  3003  3101  3199  3297  3395  3493
## [16405]  3591  3689  3787  3885  3983  4081  4179  4277  4375  4473  4571  4669
## [16417]  4767  4865  4963  5061  5159  5257  5355  5453  5551  5649  5747  5845
## [16429]  5943  6041  6139  6237  6335  6433  6531  6629  6727  6825  6923  7021
## [16441]  7119  7217  7315  7413  7511  7609  7707  7805  7903  8001  8099  8197
## [16453]  8295  8393  8491  8589  8687  8785  8883  8981  9079  9177  9275  9373
## [16465]  9471  9569  9667  9765  9863  9961 10059 10157 10255 10353 10451 10549
## [16477] 10647 10745 10843 10941 11039 11137 11235 11333 11431 11529 11627 11725
## [16489] 11823 11921 12019 12117 12215 12313 12411 12509 12607 12705 12803 12901
## [16501] 12999 13097 13195 13293 13391 13489 13587 13685 13783 13881 13979 14077
## [16513] 14175 14273 14371 14469 14567 14665 14763 14861 14959 15057 15155 15253
## [16525] 15351 15449 15547 15645 15743 15841 15939 16037 16135 16233 16331 16429
## [16537] 16527 16625 16723 16821 16919 17017 17115 17213 17311 17409 17507 17605
## [16549] 17703 17801 17899 17997 18095 18193 18291 18389 18487 18585 18683 18781
## [16561] 18879 18977 19075 19173 19271 19369 19467 19565 19663 19761 19859 19957
## [16573] 20055 20153 20251 20349 20447 20545 20643 20741 20839 20937 21035 21133
## [16585] 21231 21329 21427 21525 21623 21721 21819 21917 22015 22113 22211 22309
## [16597] 22407 22505 22603 22701 22799 22897 22995 23093 23191 23289 23387 23485
## [16609] 23583 23681 23779 23877 23975 24073 24171 24269 24367 24465 24563 24661
## [16621] 24759 24857 24955 25053 25151 25249 25347 25445 25543 25641 25739 25837
## [16633]    64   162   260   358   456   554   652   750   848   946  1044  1142
## [16645]  1240  1338  1436  1534  1632  1730  1828  1926  2024  2122  2220  2318
## [16657]  2416  2514  2612  2710  2808  2906  3004  3102  3200  3298  3396  3494
## [16669]  3592  3690  3788  3886  3984  4082  4180  4278  4376  4474  4572  4670
## [16681]  4768  4866  4964  5062  5160  5258  5356  5454  5552  5650  5748  5846
## [16693]  5944  6042  6140  6238  6336  6434  6532  6630  6728  6826  6924  7022
## [16705]  7120  7218  7316  7414  7512  7610  7708  7806  7904  8002  8100  8198
## [16717]  8296  8394  8492  8590  8688  8786  8884  8982  9080  9178  9276  9374
## [16729]  9472  9570  9668  9766  9864  9962 10060 10158 10256 10354 10452 10550
## [16741] 10648 10746 10844 10942 11040 11138 11236 11334 11432 11530 11628 11726
## [16753] 11824 11922 12020 12118 12216 12314 12412 12510 12608 12706 12804 12902
## [16765] 13000 13098 13196 13294 13392 13490 13588 13686 13784 13882 13980 14078
## [16777] 14176 14274 14372 14470 14568 14666 14764 14862 14960 15058 15156 15254
## [16789] 15352 15450 15548 15646 15744 15842 15940 16038 16136 16234 16332 16430
## [16801] 16528 16626 16724 16822 16920 17018 17116 17214 17312 17410 17508 17606
## [16813] 17704 17802 17900 17998 18096 18194 18292 18390 18488 18586 18684 18782
## [16825] 18880 18978 19076 19174 19272 19370 19468 19566 19664 19762 19860 19958
## [16837] 20056 20154 20252 20350 20448 20546 20644 20742 20840 20938 21036 21134
## [16849] 21232 21330 21428 21526 21624 21722 21820 21918 22016 22114 22212 22310
## [16861] 22408 22506 22604 22702 22800 22898 22996 23094 23192 23290 23388 23486
## [16873] 23584 23682 23780 23878 23976 24074 24172 24270 24368 24466 24564 24662
## [16885] 24760 24858 24956 25054 25152 25250 25348 25446 25544 25642 25740 25838
## [16897]    65   163   261   359   457   555   653   751   849   947  1045  1143
## [16909]  1241  1339  1437  1535  1633  1731  1829  1927  2025  2123  2221  2319
## [16921]  2417  2515  2613  2711  2809  2907  3005  3103  3201  3299  3397  3495
## [16933]  3593  3691  3789  3887  3985  4083  4181  4279  4377  4475  4573  4671
## [16945]  4769  4867  4965  5063  5161  5259  5357  5455  5553  5651  5749  5847
## [16957]  5945  6043  6141  6239  6337  6435  6533  6631  6729  6827  6925  7023
## [16969]  7121  7219  7317  7415  7513  7611  7709  7807  7905  8003  8101  8199
## [16981]  8297  8395  8493  8591  8689  8787  8885  8983  9081  9179  9277  9375
## [16993]  9473  9571  9669  9767  9865  9963 10061 10159 10257 10355 10453 10551
## [17005] 10649 10747 10845 10943 11041 11139 11237 11335 11433 11531 11629 11727
## [17017] 11825 11923 12021 12119 12217 12315 12413 12511 12609 12707 12805 12903
## [17029] 13001 13099 13197 13295 13393 13491 13589 13687 13785 13883 13981 14079
## [17041] 14177 14275 14373 14471 14569 14667 14765 14863 14961 15059 15157 15255
## [17053] 15353 15451 15549 15647 15745 15843 15941 16039 16137 16235 16333 16431
## [17065] 16529 16627 16725 16823 16921 17019 17117 17215 17313 17411 17509 17607
## [17077] 17705 17803 17901 17999 18097 18195 18293 18391 18489 18587 18685 18783
## [17089] 18881 18979 19077 19175 19273 19371 19469 19567 19665 19763 19861 19959
## [17101] 20057 20155 20253 20351 20449 20547 20645 20743 20841 20939 21037 21135
## [17113] 21233 21331 21429 21527 21625 21723 21821 21919 22017 22115 22213 22311
## [17125] 22409 22507 22605 22703 22801 22899 22997 23095 23193 23291 23389 23487
## [17137] 23585 23683 23781 23879 23977 24075 24173 24271 24369 24467 24565 24663
## [17149] 24761 24859 24957 25055 25153 25251 25349 25447 25545 25643 25741 25839
## [17161]    66   164   262   360   458   556   654   752   850   948  1046  1144
## [17173]  1242  1340  1438  1536  1634  1732  1830  1928  2026  2124  2222  2320
## [17185]  2418  2516  2614  2712  2810  2908  3006  3104  3202  3300  3398  3496
## [17197]  3594  3692  3790  3888  3986  4084  4182  4280  4378  4476  4574  4672
## [17209]  4770  4868  4966  5064  5162  5260  5358  5456  5554  5652  5750  5848
## [17221]  5946  6044  6142  6240  6338  6436  6534  6632  6730  6828  6926  7024
## [17233]  7122  7220  7318  7416  7514  7612  7710  7808  7906  8004  8102  8200
## [17245]  8298  8396  8494  8592  8690  8788  8886  8984  9082  9180  9278  9376
## [17257]  9474  9572  9670  9768  9866  9964 10062 10160 10258 10356 10454 10552
## [17269] 10650 10748 10846 10944 11042 11140 11238 11336 11434 11532 11630 11728
## [17281] 11826 11924 12022 12120 12218 12316 12414 12512 12610 12708 12806 12904
## [17293] 13002 13100 13198 13296 13394 13492 13590 13688 13786 13884 13982 14080
## [17305] 14178 14276 14374 14472 14570 14668 14766 14864 14962 15060 15158 15256
## [17317] 15354 15452 15550 15648 15746 15844 15942 16040 16138 16236 16334 16432
## [17329] 16530 16628 16726 16824 16922 17020 17118 17216 17314 17412 17510 17608
## [17341] 17706 17804 17902 18000 18098 18196 18294 18392 18490 18588 18686 18784
## [17353] 18882 18980 19078 19176 19274 19372 19470 19568 19666 19764 19862 19960
## [17365] 20058 20156 20254 20352 20450 20548 20646 20744 20842 20940 21038 21136
## [17377] 21234 21332 21430 21528 21626 21724 21822 21920 22018 22116 22214 22312
## [17389] 22410 22508 22606 22704 22802 22900 22998 23096 23194 23292 23390 23488
## [17401] 23586 23684 23782 23880 23978 24076 24174 24272 24370 24468 24566 24664
## [17413] 24762 24860 24958 25056 25154 25252 25350 25448 25546 25644 25742 25840
## [17425]    67   165   263   361   459   557   655   753   851   949  1047  1145
## [17437]  1243  1341  1439  1537  1635  1733  1831  1929  2027  2125  2223  2321
## [17449]  2419  2517  2615  2713  2811  2909  3007  3105  3203  3301  3399  3497
## [17461]  3595  3693  3791  3889  3987  4085  4183  4281  4379  4477  4575  4673
## [17473]  4771  4869  4967  5065  5163  5261  5359  5457  5555  5653  5751  5849
## [17485]  5947  6045  6143  6241  6339  6437  6535  6633  6731  6829  6927  7025
## [17497]  7123  7221  7319  7417  7515  7613  7711  7809  7907  8005  8103  8201
## [17509]  8299  8397  8495  8593  8691  8789  8887  8985  9083  9181  9279  9377
## [17521]  9475  9573  9671  9769  9867  9965 10063 10161 10259 10357 10455 10553
## [17533] 10651 10749 10847 10945 11043 11141 11239 11337 11435 11533 11631 11729
## [17545] 11827 11925 12023 12121 12219 12317 12415 12513 12611 12709 12807 12905
## [17557] 13003 13101 13199 13297 13395 13493 13591 13689 13787 13885 13983 14081
## [17569] 14179 14277 14375 14473 14571 14669 14767 14865 14963 15061 15159 15257
## [17581] 15355 15453 15551 15649 15747 15845 15943 16041 16139 16237 16335 16433
## [17593] 16531 16629 16727 16825 16923 17021 17119 17217 17315 17413 17511 17609
## [17605] 17707 17805 17903 18001 18099 18197 18295 18393 18491 18589 18687 18785
## [17617] 18883 18981 19079 19177 19275 19373 19471 19569 19667 19765 19863 19961
## [17629] 20059 20157 20255 20353 20451 20549 20647 20745 20843 20941 21039 21137
## [17641] 21235 21333 21431 21529 21627 21725 21823 21921 22019 22117 22215 22313
## [17653] 22411 22509 22607 22705 22803 22901 22999 23097 23195 23293 23391 23489
## [17665] 23587 23685 23783 23881 23979 24077 24175 24273 24371 24469 24567 24665
## [17677] 24763 24861 24959 25057 25155 25253 25351 25449 25547 25645 25743 25841
## [17689]    68   166   264   362   460   558   656   754   852   950  1048  1146
## [17701]  1244  1342  1440  1538  1636  1734  1832  1930  2028  2126  2224  2322
## [17713]  2420  2518  2616  2714  2812  2910  3008  3106  3204  3302  3400  3498
## [17725]  3596  3694  3792  3890  3988  4086  4184  4282  4380  4478  4576  4674
## [17737]  4772  4870  4968  5066  5164  5262  5360  5458  5556  5654  5752  5850
## [17749]  5948  6046  6144  6242  6340  6438  6536  6634  6732  6830  6928  7026
## [17761]  7124  7222  7320  7418  7516  7614  7712  7810  7908  8006  8104  8202
## [17773]  8300  8398  8496  8594  8692  8790  8888  8986  9084  9182  9280  9378
## [17785]  9476  9574  9672  9770  9868  9966 10064 10162 10260 10358 10456 10554
## [17797] 10652 10750 10848 10946 11044 11142 11240 11338 11436 11534 11632 11730
## [17809] 11828 11926 12024 12122 12220 12318 12416 12514 12612 12710 12808 12906
## [17821] 13004 13102 13200 13298 13396 13494 13592 13690 13788 13886 13984 14082
## [17833] 14180 14278 14376 14474 14572 14670 14768 14866 14964 15062 15160 15258
## [17845] 15356 15454 15552 15650 15748 15846 15944 16042 16140 16238 16336 16434
## [17857] 16532 16630 16728 16826 16924 17022 17120 17218 17316 17414 17512 17610
## [17869] 17708 17806 17904 18002 18100 18198 18296 18394 18492 18590 18688 18786
## [17881] 18884 18982 19080 19178 19276 19374 19472 19570 19668 19766 19864 19962
## [17893] 20060 20158 20256 20354 20452 20550 20648 20746 20844 20942 21040 21138
## [17905] 21236 21334 21432 21530 21628 21726 21824 21922 22020 22118 22216 22314
## [17917] 22412 22510 22608 22706 22804 22902 23000 23098 23196 23294 23392 23490
## [17929] 23588 23686 23784 23882 23980 24078 24176 24274 24372 24470 24568 24666
## [17941] 24764 24862 24960 25058 25156 25254 25352 25450 25548 25646 25744 25842
## [17953]    69   167   265   363   461   559   657   755   853   951  1049  1147
## [17965]  1245  1343  1441  1539  1637  1735  1833  1931  2029  2127  2225  2323
## [17977]  2421  2519  2617  2715  2813  2911  3009  3107  3205  3303  3401  3499
## [17989]  3597  3695  3793  3891  3989  4087  4185  4283  4381  4479  4577  4675
## [18001]  4773  4871  4969  5067  5165  5263  5361  5459  5557  5655  5753  5851
## [18013]  5949  6047  6145  6243  6341  6439  6537  6635  6733  6831  6929  7027
## [18025]  7125  7223  7321  7419  7517  7615  7713  7811  7909  8007  8105  8203
## [18037]  8301  8399  8497  8595  8693  8791  8889  8987  9085  9183  9281  9379
## [18049]  9477  9575  9673  9771  9869  9967 10065 10163 10261 10359 10457 10555
## [18061] 10653 10751 10849 10947 11045 11143 11241 11339 11437 11535 11633 11731
## [18073] 11829 11927 12025 12123 12221 12319 12417 12515 12613 12711 12809 12907
## [18085] 13005 13103 13201 13299 13397 13495 13593 13691 13789 13887 13985 14083
## [18097] 14181 14279 14377 14475 14573 14671 14769 14867 14965 15063 15161 15259
## [18109] 15357 15455 15553 15651 15749 15847 15945 16043 16141 16239 16337 16435
## [18121] 16533 16631 16729 16827 16925 17023 17121 17219 17317 17415 17513 17611
## [18133] 17709 17807 17905 18003 18101 18199 18297 18395 18493 18591 18689 18787
## [18145] 18885 18983 19081 19179 19277 19375 19473 19571 19669 19767 19865 19963
## [18157] 20061 20159 20257 20355 20453 20551 20649 20747 20845 20943 21041 21139
## [18169] 21237 21335 21433 21531 21629 21727 21825 21923 22021 22119 22217 22315
## [18181] 22413 22511 22609 22707 22805 22903 23001 23099 23197 23295 23393 23491
## [18193] 23589 23687 23785 23883 23981 24079 24177 24275 24373 24471 24569 24667
## [18205] 24765 24863 24961 25059 25157 25255 25353 25451 25549 25647 25745 25843
## [18217]    70   168   266   364   462   560   658   756   854   952  1050  1148
## [18229]  1246  1344  1442  1540  1638  1736  1834  1932  2030  2128  2226  2324
## [18241]  2422  2520  2618  2716  2814  2912  3010  3108  3206  3304  3402  3500
## [18253]  3598  3696  3794  3892  3990  4088  4186  4284  4382  4480  4578  4676
## [18265]  4774  4872  4970  5068  5166  5264  5362  5460  5558  5656  5754  5852
## [18277]  5950  6048  6146  6244  6342  6440  6538  6636  6734  6832  6930  7028
## [18289]  7126  7224  7322  7420  7518  7616  7714  7812  7910  8008  8106  8204
## [18301]  8302  8400  8498  8596  8694  8792  8890  8988  9086  9184  9282  9380
## [18313]  9478  9576  9674  9772  9870  9968 10066 10164 10262 10360 10458 10556
## [18325] 10654 10752 10850 10948 11046 11144 11242 11340 11438 11536 11634 11732
## [18337] 11830 11928 12026 12124 12222 12320 12418 12516 12614 12712 12810 12908
## [18349] 13006 13104 13202 13300 13398 13496 13594 13692 13790 13888 13986 14084
## [18361] 14182 14280 14378 14476 14574 14672 14770 14868 14966 15064 15162 15260
## [18373] 15358 15456 15554 15652 15750 15848 15946 16044 16142 16240 16338 16436
## [18385] 16534 16632 16730 16828 16926 17024 17122 17220 17318 17416 17514 17612
## [18397] 17710 17808 17906 18004 18102 18200 18298 18396 18494 18592 18690 18788
## [18409] 18886 18984 19082 19180 19278 19376 19474 19572 19670 19768 19866 19964
## [18421] 20062 20160 20258 20356 20454 20552 20650 20748 20846 20944 21042 21140
## [18433] 21238 21336 21434 21532 21630 21728 21826 21924 22022 22120 22218 22316
## [18445] 22414 22512 22610 22708 22806 22904 23002 23100 23198 23296 23394 23492
## [18457] 23590 23688 23786 23884 23982 24080 24178 24276 24374 24472 24570 24668
## [18469] 24766 24864 24962 25060 25158 25256 25354 25452 25550 25648 25746 25844
## [18481]    71   169   267   365   463   561   659   757   855   953  1051  1149
## [18493]  1247  1345  1443  1541  1639  1737  1835  1933  2031  2129  2227  2325
## [18505]  2423  2521  2619  2717  2815  2913  3011  3109  3207  3305  3403  3501
## [18517]  3599  3697  3795  3893  3991  4089  4187  4285  4383  4481  4579  4677
## [18529]  4775  4873  4971  5069  5167  5265  5363  5461  5559  5657  5755  5853
## [18541]  5951  6049  6147  6245  6343  6441  6539  6637  6735  6833  6931  7029
## [18553]  7127  7225  7323  7421  7519  7617  7715  7813  7911  8009  8107  8205
## [18565]  8303  8401  8499  8597  8695  8793  8891  8989  9087  9185  9283  9381
## [18577]  9479  9577  9675  9773  9871  9969 10067 10165 10263 10361 10459 10557
## [18589] 10655 10753 10851 10949 11047 11145 11243 11341 11439 11537 11635 11733
## [18601] 11831 11929 12027 12125 12223 12321 12419 12517 12615 12713 12811 12909
## [18613] 13007 13105 13203 13301 13399 13497 13595 13693 13791 13889 13987 14085
## [18625] 14183 14281 14379 14477 14575 14673 14771 14869 14967 15065 15163 15261
## [18637] 15359 15457 15555 15653 15751 15849 15947 16045 16143 16241 16339 16437
## [18649] 16535 16633 16731 16829 16927 17025 17123 17221 17319 17417 17515 17613
## [18661] 17711 17809 17907 18005 18103 18201 18299 18397 18495 18593 18691 18789
## [18673] 18887 18985 19083 19181 19279 19377 19475 19573 19671 19769 19867 19965
## [18685] 20063 20161 20259 20357 20455 20553 20651 20749 20847 20945 21043 21141
## [18697] 21239 21337 21435 21533 21631 21729 21827 21925 22023 22121 22219 22317
## [18709] 22415 22513 22611 22709 22807 22905 23003 23101 23199 23297 23395 23493
## [18721] 23591 23689 23787 23885 23983 24081 24179 24277 24375 24473 24571 24669
## [18733] 24767 24865 24963 25061 25159 25257 25355 25453 25551 25649 25747 25845
## [18745]    72   170   268   366   464   562   660   758   856   954  1052  1150
## [18757]  1248  1346  1444  1542  1640  1738  1836  1934  2032  2130  2228  2326
## [18769]  2424  2522  2620  2718  2816  2914  3012  3110  3208  3306  3404  3502
## [18781]  3600  3698  3796  3894  3992  4090  4188  4286  4384  4482  4580  4678
## [18793]  4776  4874  4972  5070  5168  5266  5364  5462  5560  5658  5756  5854
## [18805]  5952  6050  6148  6246  6344  6442  6540  6638  6736  6834  6932  7030
## [18817]  7128  7226  7324  7422  7520  7618  7716  7814  7912  8010  8108  8206
## [18829]  8304  8402  8500  8598  8696  8794  8892  8990  9088  9186  9284  9382
## [18841]  9480  9578  9676  9774  9872  9970 10068 10166 10264 10362 10460 10558
## [18853] 10656 10754 10852 10950 11048 11146 11244 11342 11440 11538 11636 11734
## [18865] 11832 11930 12028 12126 12224 12322 12420 12518 12616 12714 12812 12910
## [18877] 13008 13106 13204 13302 13400 13498 13596 13694 13792 13890 13988 14086
## [18889] 14184 14282 14380 14478 14576 14674 14772 14870 14968 15066 15164 15262
## [18901] 15360 15458 15556 15654 15752 15850 15948 16046 16144 16242 16340 16438
## [18913] 16536 16634 16732 16830 16928 17026 17124 17222 17320 17418 17516 17614
## [18925] 17712 17810 17908 18006 18104 18202 18300 18398 18496 18594 18692 18790
## [18937] 18888 18986 19084 19182 19280 19378 19476 19574 19672 19770 19868 19966
## [18949] 20064 20162 20260 20358 20456 20554 20652 20750 20848 20946 21044 21142
## [18961] 21240 21338 21436 21534 21632 21730 21828 21926 22024 22122 22220 22318
## [18973] 22416 22514 22612 22710 22808 22906 23004 23102 23200 23298 23396 23494
## [18985] 23592 23690 23788 23886 23984 24082 24180 24278 24376 24474 24572 24670
## [18997] 24768 24866 24964 25062 25160 25258 25356 25454 25552 25650 25748 25846
## [19009]    73   171   269   367   465   563   661   759   857   955  1053  1151
## [19021]  1249  1347  1445  1543  1641  1739  1837  1935  2033  2131  2229  2327
## [19033]  2425  2523  2621  2719  2817  2915  3013  3111  3209  3307  3405  3503
## [19045]  3601  3699  3797  3895  3993  4091  4189  4287  4385  4483  4581  4679
## [19057]  4777  4875  4973  5071  5169  5267  5365  5463  5561  5659  5757  5855
## [19069]  5953  6051  6149  6247  6345  6443  6541  6639  6737  6835  6933  7031
## [19081]  7129  7227  7325  7423  7521  7619  7717  7815  7913  8011  8109  8207
## [19093]  8305  8403  8501  8599  8697  8795  8893  8991  9089  9187  9285  9383
## [19105]  9481  9579  9677  9775  9873  9971 10069 10167 10265 10363 10461 10559
## [19117] 10657 10755 10853 10951 11049 11147 11245 11343 11441 11539 11637 11735
## [19129] 11833 11931 12029 12127 12225 12323 12421 12519 12617 12715 12813 12911
## [19141] 13009 13107 13205 13303 13401 13499 13597 13695 13793 13891 13989 14087
## [19153] 14185 14283 14381 14479 14577 14675 14773 14871 14969 15067 15165 15263
## [19165] 15361 15459 15557 15655 15753 15851 15949 16047 16145 16243 16341 16439
## [19177] 16537 16635 16733 16831 16929 17027 17125 17223 17321 17419 17517 17615
## [19189] 17713 17811 17909 18007 18105 18203 18301 18399 18497 18595 18693 18791
## [19201] 18889 18987 19085 19183 19281 19379 19477 19575 19673 19771 19869 19967
## [19213] 20065 20163 20261 20359 20457 20555 20653 20751 20849 20947 21045 21143
## [19225] 21241 21339 21437 21535 21633 21731 21829 21927 22025 22123 22221 22319
## [19237] 22417 22515 22613 22711 22809 22907 23005 23103 23201 23299 23397 23495
## [19249] 23593 23691 23789 23887 23985 24083 24181 24279 24377 24475 24573 24671
## [19261] 24769 24867 24965 25063 25161 25259 25357 25455 25553 25651 25749 25847
## [19273]    74   172   270   368   466   564   662   760   858   956  1054  1152
## [19285]  1250  1348  1446  1544  1642  1740  1838  1936  2034  2132  2230  2328
## [19297]  2426  2524  2622  2720  2818  2916  3014  3112  3210  3308  3406  3504
## [19309]  3602  3700  3798  3896  3994  4092  4190  4288  4386  4484  4582  4680
## [19321]  4778  4876  4974  5072  5170  5268  5366  5464  5562  5660  5758  5856
## [19333]  5954  6052  6150  6248  6346  6444  6542  6640  6738  6836  6934  7032
## [19345]  7130  7228  7326  7424  7522  7620  7718  7816  7914  8012  8110  8208
## [19357]  8306  8404  8502  8600  8698  8796  8894  8992  9090  9188  9286  9384
## [19369]  9482  9580  9678  9776  9874  9972 10070 10168 10266 10364 10462 10560
## [19381] 10658 10756 10854 10952 11050 11148 11246 11344 11442 11540 11638 11736
## [19393] 11834 11932 12030 12128 12226 12324 12422 12520 12618 12716 12814 12912
## [19405] 13010 13108 13206 13304 13402 13500 13598 13696 13794 13892 13990 14088
## [19417] 14186 14284 14382 14480 14578 14676 14774 14872 14970 15068 15166 15264
## [19429] 15362 15460 15558 15656 15754 15852 15950 16048 16146 16244 16342 16440
## [19441] 16538 16636 16734 16832 16930 17028 17126 17224 17322 17420 17518 17616
## [19453] 17714 17812 17910 18008 18106 18204 18302 18400 18498 18596 18694 18792
## [19465] 18890 18988 19086 19184 19282 19380 19478 19576 19674 19772 19870 19968
## [19477] 20066 20164 20262 20360 20458 20556 20654 20752 20850 20948 21046 21144
## [19489] 21242 21340 21438 21536 21634 21732 21830 21928 22026 22124 22222 22320
## [19501] 22418 22516 22614 22712 22810 22908 23006 23104 23202 23300 23398 23496
## [19513] 23594 23692 23790 23888 23986 24084 24182 24280 24378 24476 24574 24672
## [19525] 24770 24868 24966 25064 25162 25260 25358 25456 25554 25652 25750 25848
## [19537]    75   173   271   369   467   565   663   761   859   957  1055  1153
## [19549]  1251  1349  1447  1545  1643  1741  1839  1937  2035  2133  2231  2329
## [19561]  2427  2525  2623  2721  2819  2917  3015  3113  3211  3309  3407  3505
## [19573]  3603  3701  3799  3897  3995  4093  4191  4289  4387  4485  4583  4681
## [19585]  4779  4877  4975  5073  5171  5269  5367  5465  5563  5661  5759  5857
## [19597]  5955  6053  6151  6249  6347  6445  6543  6641  6739  6837  6935  7033
## [19609]  7131  7229  7327  7425  7523  7621  7719  7817  7915  8013  8111  8209
## [19621]  8307  8405  8503  8601  8699  8797  8895  8993  9091  9189  9287  9385
## [19633]  9483  9581  9679  9777  9875  9973 10071 10169 10267 10365 10463 10561
## [19645] 10659 10757 10855 10953 11051 11149 11247 11345 11443 11541 11639 11737
## [19657] 11835 11933 12031 12129 12227 12325 12423 12521 12619 12717 12815 12913
## [19669] 13011 13109 13207 13305 13403 13501 13599 13697 13795 13893 13991 14089
## [19681] 14187 14285 14383 14481 14579 14677 14775 14873 14971 15069 15167 15265
## [19693] 15363 15461 15559 15657 15755 15853 15951 16049 16147 16245 16343 16441
## [19705] 16539 16637 16735 16833 16931 17029 17127 17225 17323 17421 17519 17617
## [19717] 17715 17813 17911 18009 18107 18205 18303 18401 18499 18597 18695 18793
## [19729] 18891 18989 19087 19185 19283 19381 19479 19577 19675 19773 19871 19969
## [19741] 20067 20165 20263 20361 20459 20557 20655 20753 20851 20949 21047 21145
## [19753] 21243 21341 21439 21537 21635 21733 21831 21929 22027 22125 22223 22321
## [19765] 22419 22517 22615 22713 22811 22909 23007 23105 23203 23301 23399 23497
## [19777] 23595 23693 23791 23889 23987 24085 24183 24281 24379 24477 24575 24673
## [19789] 24771 24869 24967 25065 25163 25261 25359 25457 25555 25653 25751 25849
## [19801]    76   174   272   370   468   566   664   762   860   958  1056  1154
## [19813]  1252  1350  1448  1546  1644  1742  1840  1938  2036  2134  2232  2330
## [19825]  2428  2526  2624  2722  2820  2918  3016  3114  3212  3310  3408  3506
## [19837]  3604  3702  3800  3898  3996  4094  4192  4290  4388  4486  4584  4682
## [19849]  4780  4878  4976  5074  5172  5270  5368  5466  5564  5662  5760  5858
## [19861]  5956  6054  6152  6250  6348  6446  6544  6642  6740  6838  6936  7034
## [19873]  7132  7230  7328  7426  7524  7622  7720  7818  7916  8014  8112  8210
## [19885]  8308  8406  8504  8602  8700  8798  8896  8994  9092  9190  9288  9386
## [19897]  9484  9582  9680  9778  9876  9974 10072 10170 10268 10366 10464 10562
## [19909] 10660 10758 10856 10954 11052 11150 11248 11346 11444 11542 11640 11738
## [19921] 11836 11934 12032 12130 12228 12326 12424 12522 12620 12718 12816 12914
## [19933] 13012 13110 13208 13306 13404 13502 13600 13698 13796 13894 13992 14090
## [19945] 14188 14286 14384 14482 14580 14678 14776 14874 14972 15070 15168 15266
## [19957] 15364 15462 15560 15658 15756 15854 15952 16050 16148 16246 16344 16442
## [19969] 16540 16638 16736 16834 16932 17030 17128 17226 17324 17422 17520 17618
## [19981] 17716 17814 17912 18010 18108 18206 18304 18402 18500 18598 18696 18794
## [19993] 18892 18990 19088 19186 19284 19382 19480 19578 19676 19774 19872 19970
## [20005] 20068 20166 20264 20362 20460 20558 20656 20754 20852 20950 21048 21146
## [20017] 21244 21342 21440 21538 21636 21734 21832 21930 22028 22126 22224 22322
## [20029] 22420 22518 22616 22714 22812 22910 23008 23106 23204 23302 23400 23498
## [20041] 23596 23694 23792 23890 23988 24086 24184 24282 24380 24478 24576 24674
## [20053] 24772 24870 24968 25066 25164 25262 25360 25458 25556 25654 25752 25850
## [20065]    77   175   273   371   469   567   665   763   861   959  1057  1155
## [20077]  1253  1351  1449  1547  1645  1743  1841  1939  2037  2135  2233  2331
## [20089]  2429  2527  2625  2723  2821  2919  3017  3115  3213  3311  3409  3507
## [20101]  3605  3703  3801  3899  3997  4095  4193  4291  4389  4487  4585  4683
## [20113]  4781  4879  4977  5075  5173  5271  5369  5467  5565  5663  5761  5859
## [20125]  5957  6055  6153  6251  6349  6447  6545  6643  6741  6839  6937  7035
## [20137]  7133  7231  7329  7427  7525  7623  7721  7819  7917  8015  8113  8211
## [20149]  8309  8407  8505  8603  8701  8799  8897  8995  9093  9191  9289  9387
## [20161]  9485  9583  9681  9779  9877  9975 10073 10171 10269 10367 10465 10563
## [20173] 10661 10759 10857 10955 11053 11151 11249 11347 11445 11543 11641 11739
## [20185] 11837 11935 12033 12131 12229 12327 12425 12523 12621 12719 12817 12915
## [20197] 13013 13111 13209 13307 13405 13503 13601 13699 13797 13895 13993 14091
## [20209] 14189 14287 14385 14483 14581 14679 14777 14875 14973 15071 15169 15267
## [20221] 15365 15463 15561 15659 15757 15855 15953 16051 16149 16247 16345 16443
## [20233] 16541 16639 16737 16835 16933 17031 17129 17227 17325 17423 17521 17619
## [20245] 17717 17815 17913 18011 18109 18207 18305 18403 18501 18599 18697 18795
## [20257] 18893 18991 19089 19187 19285 19383 19481 19579 19677 19775 19873 19971
## [20269] 20069 20167 20265 20363 20461 20559 20657 20755 20853 20951 21049 21147
## [20281] 21245 21343 21441 21539 21637 21735 21833 21931 22029 22127 22225 22323
## [20293] 22421 22519 22617 22715 22813 22911 23009 23107 23205 23303 23401 23499
## [20305] 23597 23695 23793 23891 23989 24087 24185 24283 24381 24479 24577 24675
## [20317] 24773 24871 24969 25067 25165 25263 25361 25459 25557 25655 25753 25851
## [20329]    78   176   274   372   470   568   666   764   862   960  1058  1156
## [20341]  1254  1352  1450  1548  1646  1744  1842  1940  2038  2136  2234  2332
## [20353]  2430  2528  2626  2724  2822  2920  3018  3116  3214  3312  3410  3508
## [20365]  3606  3704  3802  3900  3998  4096  4194  4292  4390  4488  4586  4684
## [20377]  4782  4880  4978  5076  5174  5272  5370  5468  5566  5664  5762  5860
## [20389]  5958  6056  6154  6252  6350  6448  6546  6644  6742  6840  6938  7036
## [20401]  7134  7232  7330  7428  7526  7624  7722  7820  7918  8016  8114  8212
## [20413]  8310  8408  8506  8604  8702  8800  8898  8996  9094  9192  9290  9388
## [20425]  9486  9584  9682  9780  9878  9976 10074 10172 10270 10368 10466 10564
## [20437] 10662 10760 10858 10956 11054 11152 11250 11348 11446 11544 11642 11740
## [20449] 11838 11936 12034 12132 12230 12328 12426 12524 12622 12720 12818 12916
## [20461] 13014 13112 13210 13308 13406 13504 13602 13700 13798 13896 13994 14092
## [20473] 14190 14288 14386 14484 14582 14680 14778 14876 14974 15072 15170 15268
## [20485] 15366 15464 15562 15660 15758 15856 15954 16052 16150 16248 16346 16444
## [20497] 16542 16640 16738 16836 16934 17032 17130 17228 17326 17424 17522 17620
## [20509] 17718 17816 17914 18012 18110 18208 18306 18404 18502 18600 18698 18796
## [20521] 18894 18992 19090 19188 19286 19384 19482 19580 19678 19776 19874 19972
## [20533] 20070 20168 20266 20364 20462 20560 20658 20756 20854 20952 21050 21148
## [20545] 21246 21344 21442 21540 21638 21736 21834 21932 22030 22128 22226 22324
## [20557] 22422 22520 22618 22716 22814 22912 23010 23108 23206 23304 23402 23500
## [20569] 23598 23696 23794 23892 23990 24088 24186 24284 24382 24480 24578 24676
## [20581] 24774 24872 24970 25068 25166 25264 25362 25460 25558 25656 25754 25852
## [20593]    79   177   275   373   471   569   667   765   863   961  1059  1157
## [20605]  1255  1353  1451  1549  1647  1745  1843  1941  2039  2137  2235  2333
## [20617]  2431  2529  2627  2725  2823  2921  3019  3117  3215  3313  3411  3509
## [20629]  3607  3705  3803  3901  3999  4097  4195  4293  4391  4489  4587  4685
## [20641]  4783  4881  4979  5077  5175  5273  5371  5469  5567  5665  5763  5861
## [20653]  5959  6057  6155  6253  6351  6449  6547  6645  6743  6841  6939  7037
## [20665]  7135  7233  7331  7429  7527  7625  7723  7821  7919  8017  8115  8213
## [20677]  8311  8409  8507  8605  8703  8801  8899  8997  9095  9193  9291  9389
## [20689]  9487  9585  9683  9781  9879  9977 10075 10173 10271 10369 10467 10565
## [20701] 10663 10761 10859 10957 11055 11153 11251 11349 11447 11545 11643 11741
## [20713] 11839 11937 12035 12133 12231 12329 12427 12525 12623 12721 12819 12917
## [20725] 13015 13113 13211 13309 13407 13505 13603 13701 13799 13897 13995 14093
## [20737] 14191 14289 14387 14485 14583 14681 14779 14877 14975 15073 15171 15269
## [20749] 15367 15465 15563 15661 15759 15857 15955 16053 16151 16249 16347 16445
## [20761] 16543 16641 16739 16837 16935 17033 17131 17229 17327 17425 17523 17621
## [20773] 17719 17817 17915 18013 18111 18209 18307 18405 18503 18601 18699 18797
## [20785] 18895 18993 19091 19189 19287 19385 19483 19581 19679 19777 19875 19973
## [20797] 20071 20169 20267 20365 20463 20561 20659 20757 20855 20953 21051 21149
## [20809] 21247 21345 21443 21541 21639 21737 21835 21933 22031 22129 22227 22325
## [20821] 22423 22521 22619 22717 22815 22913 23011 23109 23207 23305 23403 23501
## [20833] 23599 23697 23795 23893 23991 24089 24187 24285 24383 24481 24579 24677
## [20845] 24775 24873 24971 25069 25167 25265 25363 25461 25559 25657 25755 25853
## [20857]    80   178   276   374   472   570   668   766   864   962  1060  1158
## [20869]  1256  1354  1452  1550  1648  1746  1844  1942  2040  2138  2236  2334
## [20881]  2432  2530  2628  2726  2824  2922  3020  3118  3216  3314  3412  3510
## [20893]  3608  3706  3804  3902  4000  4098  4196  4294  4392  4490  4588  4686
## [20905]  4784  4882  4980  5078  5176  5274  5372  5470  5568  5666  5764  5862
## [20917]  5960  6058  6156  6254  6352  6450  6548  6646  6744  6842  6940  7038
## [20929]  7136  7234  7332  7430  7528  7626  7724  7822  7920  8018  8116  8214
## [20941]  8312  8410  8508  8606  8704  8802  8900  8998  9096  9194  9292  9390
## [20953]  9488  9586  9684  9782  9880  9978 10076 10174 10272 10370 10468 10566
## [20965] 10664 10762 10860 10958 11056 11154 11252 11350 11448 11546 11644 11742
## [20977] 11840 11938 12036 12134 12232 12330 12428 12526 12624 12722 12820 12918
## [20989] 13016 13114 13212 13310 13408 13506 13604 13702 13800 13898 13996 14094
## [21001] 14192 14290 14388 14486 14584 14682 14780 14878 14976 15074 15172 15270
## [21013] 15368 15466 15564 15662 15760 15858 15956 16054 16152 16250 16348 16446
## [21025] 16544 16642 16740 16838 16936 17034 17132 17230 17328 17426 17524 17622
## [21037] 17720 17818 17916 18014 18112 18210 18308 18406 18504 18602 18700 18798
## [21049] 18896 18994 19092 19190 19288 19386 19484 19582 19680 19778 19876 19974
## [21061] 20072 20170 20268 20366 20464 20562 20660 20758 20856 20954 21052 21150
## [21073] 21248 21346 21444 21542 21640 21738 21836 21934 22032 22130 22228 22326
## [21085] 22424 22522 22620 22718 22816 22914 23012 23110 23208 23306 23404 23502
## [21097] 23600 23698 23796 23894 23992 24090 24188 24286 24384 24482 24580 24678
## [21109] 24776 24874 24972 25070 25168 25266 25364 25462 25560 25658 25756 25854
## [21121]    81   179   277   375   473   571   669   767   865   963  1061  1159
## [21133]  1257  1355  1453  1551  1649  1747  1845  1943  2041  2139  2237  2335
## [21145]  2433  2531  2629  2727  2825  2923  3021  3119  3217  3315  3413  3511
## [21157]  3609  3707  3805  3903  4001  4099  4197  4295  4393  4491  4589  4687
## [21169]  4785  4883  4981  5079  5177  5275  5373  5471  5569  5667  5765  5863
## [21181]  5961  6059  6157  6255  6353  6451  6549  6647  6745  6843  6941  7039
## [21193]  7137  7235  7333  7431  7529  7627  7725  7823  7921  8019  8117  8215
## [21205]  8313  8411  8509  8607  8705  8803  8901  8999  9097  9195  9293  9391
## [21217]  9489  9587  9685  9783  9881  9979 10077 10175 10273 10371 10469 10567
## [21229] 10665 10763 10861 10959 11057 11155 11253 11351 11449 11547 11645 11743
## [21241] 11841 11939 12037 12135 12233 12331 12429 12527 12625 12723 12821 12919
## [21253] 13017 13115 13213 13311 13409 13507 13605 13703 13801 13899 13997 14095
## [21265] 14193 14291 14389 14487 14585 14683 14781 14879 14977 15075 15173 15271
## [21277] 15369 15467 15565 15663 15761 15859 15957 16055 16153 16251 16349 16447
## [21289] 16545 16643 16741 16839 16937 17035 17133 17231 17329 17427 17525 17623
## [21301] 17721 17819 17917 18015 18113 18211 18309 18407 18505 18603 18701 18799
## [21313] 18897 18995 19093 19191 19289 19387 19485 19583 19681 19779 19877 19975
## [21325] 20073 20171 20269 20367 20465 20563 20661 20759 20857 20955 21053 21151
## [21337] 21249 21347 21445 21543 21641 21739 21837 21935 22033 22131 22229 22327
## [21349] 22425 22523 22621 22719 22817 22915 23013 23111 23209 23307 23405 23503
## [21361] 23601 23699 23797 23895 23993 24091 24189 24287 24385 24483 24581 24679
## [21373] 24777 24875 24973 25071 25169 25267 25365 25463 25561 25659 25757 25855
## [21385]    82   180   278   376   474   572   670   768   866   964  1062  1160
## [21397]  1258  1356  1454  1552  1650  1748  1846  1944  2042  2140  2238  2336
## [21409]  2434  2532  2630  2728  2826  2924  3022  3120  3218  3316  3414  3512
## [21421]  3610  3708  3806  3904  4002  4100  4198  4296  4394  4492  4590  4688
## [21433]  4786  4884  4982  5080  5178  5276  5374  5472  5570  5668  5766  5864
## [21445]  5962  6060  6158  6256  6354  6452  6550  6648  6746  6844  6942  7040
## [21457]  7138  7236  7334  7432  7530  7628  7726  7824  7922  8020  8118  8216
## [21469]  8314  8412  8510  8608  8706  8804  8902  9000  9098  9196  9294  9392
## [21481]  9490  9588  9686  9784  9882  9980 10078 10176 10274 10372 10470 10568
## [21493] 10666 10764 10862 10960 11058 11156 11254 11352 11450 11548 11646 11744
## [21505] 11842 11940 12038 12136 12234 12332 12430 12528 12626 12724 12822 12920
## [21517] 13018 13116 13214 13312 13410 13508 13606 13704 13802 13900 13998 14096
## [21529] 14194 14292 14390 14488 14586 14684 14782 14880 14978 15076 15174 15272
## [21541] 15370 15468 15566 15664 15762 15860 15958 16056 16154 16252 16350 16448
## [21553] 16546 16644 16742 16840 16938 17036 17134 17232 17330 17428 17526 17624
## [21565] 17722 17820 17918 18016 18114 18212 18310 18408 18506 18604 18702 18800
## [21577] 18898 18996 19094 19192 19290 19388 19486 19584 19682 19780 19878 19976
## [21589] 20074 20172 20270 20368 20466 20564 20662 20760 20858 20956 21054 21152
## [21601] 21250 21348 21446 21544 21642 21740 21838 21936 22034 22132 22230 22328
## [21613] 22426 22524 22622 22720 22818 22916 23014 23112 23210 23308 23406 23504
## [21625] 23602 23700 23798 23896 23994 24092 24190 24288 24386 24484 24582 24680
## [21637] 24778 24876 24974 25072 25170 25268 25366 25464 25562 25660 25758 25856
## [21649]    83   181   279   377   475   573   671   769   867   965  1063  1161
## [21661]  1259  1357  1455  1553  1651  1749  1847  1945  2043  2141  2239  2337
## [21673]  2435  2533  2631  2729  2827  2925  3023  3121  3219  3317  3415  3513
## [21685]  3611  3709  3807  3905  4003  4101  4199  4297  4395  4493  4591  4689
## [21697]  4787  4885  4983  5081  5179  5277  5375  5473  5571  5669  5767  5865
## [21709]  5963  6061  6159  6257  6355  6453  6551  6649  6747  6845  6943  7041
## [21721]  7139  7237  7335  7433  7531  7629  7727  7825  7923  8021  8119  8217
## [21733]  8315  8413  8511  8609  8707  8805  8903  9001  9099  9197  9295  9393
## [21745]  9491  9589  9687  9785  9883  9981 10079 10177 10275 10373 10471 10569
## [21757] 10667 10765 10863 10961 11059 11157 11255 11353 11451 11549 11647 11745
## [21769] 11843 11941 12039 12137 12235 12333 12431 12529 12627 12725 12823 12921
## [21781] 13019 13117 13215 13313 13411 13509 13607 13705 13803 13901 13999 14097
## [21793] 14195 14293 14391 14489 14587 14685 14783 14881 14979 15077 15175 15273
## [21805] 15371 15469 15567 15665 15763 15861 15959 16057 16155 16253 16351 16449
## [21817] 16547 16645 16743 16841 16939 17037 17135 17233 17331 17429 17527 17625
## [21829] 17723 17821 17919 18017 18115 18213 18311 18409 18507 18605 18703 18801
## [21841] 18899 18997 19095 19193 19291 19389 19487 19585 19683 19781 19879 19977
## [21853] 20075 20173 20271 20369 20467 20565 20663 20761 20859 20957 21055 21153
## [21865] 21251 21349 21447 21545 21643 21741 21839 21937 22035 22133 22231 22329
## [21877] 22427 22525 22623 22721 22819 22917 23015 23113 23211 23309 23407 23505
## [21889] 23603 23701 23799 23897 23995 24093 24191 24289 24387 24485 24583 24681
## [21901] 24779 24877 24975 25073 25171 25269 25367 25465 25563 25661 25759 25857
## [21913]    84   182   280   378   476   574   672   770   868   966  1064  1162
## [21925]  1260  1358  1456  1554  1652  1750  1848  1946  2044  2142  2240  2338
## [21937]  2436  2534  2632  2730  2828  2926  3024  3122  3220  3318  3416  3514
## [21949]  3612  3710  3808  3906  4004  4102  4200  4298  4396  4494  4592  4690
## [21961]  4788  4886  4984  5082  5180  5278  5376  5474  5572  5670  5768  5866
## [21973]  5964  6062  6160  6258  6356  6454  6552  6650  6748  6846  6944  7042
## [21985]  7140  7238  7336  7434  7532  7630  7728  7826  7924  8022  8120  8218
## [21997]  8316  8414  8512  8610  8708  8806  8904  9002  9100  9198  9296  9394
## [22009]  9492  9590  9688  9786  9884  9982 10080 10178 10276 10374 10472 10570
## [22021] 10668 10766 10864 10962 11060 11158 11256 11354 11452 11550 11648 11746
## [22033] 11844 11942 12040 12138 12236 12334 12432 12530 12628 12726 12824 12922
## [22045] 13020 13118 13216 13314 13412 13510 13608 13706 13804 13902 14000 14098
## [22057] 14196 14294 14392 14490 14588 14686 14784 14882 14980 15078 15176 15274
## [22069] 15372 15470 15568 15666 15764 15862 15960 16058 16156 16254 16352 16450
## [22081] 16548 16646 16744 16842 16940 17038 17136 17234 17332 17430 17528 17626
## [22093] 17724 17822 17920 18018 18116 18214 18312 18410 18508 18606 18704 18802
## [22105] 18900 18998 19096 19194 19292 19390 19488 19586 19684 19782 19880 19978
## [22117] 20076 20174 20272 20370 20468 20566 20664 20762 20860 20958 21056 21154
## [22129] 21252 21350 21448 21546 21644 21742 21840 21938 22036 22134 22232 22330
## [22141] 22428 22526 22624 22722 22820 22918 23016 23114 23212 23310 23408 23506
## [22153] 23604 23702 23800 23898 23996 24094 24192 24290 24388 24486 24584 24682
## [22165] 24780 24878 24976 25074 25172 25270 25368 25466 25564 25662 25760 25858
## [22177]    85   183   281   379   477   575   673   771   869   967  1065  1163
## [22189]  1261  1359  1457  1555  1653  1751  1849  1947  2045  2143  2241  2339
## [22201]  2437  2535  2633  2731  2829  2927  3025  3123  3221  3319  3417  3515
## [22213]  3613  3711  3809  3907  4005  4103  4201  4299  4397  4495  4593  4691
## [22225]  4789  4887  4985  5083  5181  5279  5377  5475  5573  5671  5769  5867
## [22237]  5965  6063  6161  6259  6357  6455  6553  6651  6749  6847  6945  7043
## [22249]  7141  7239  7337  7435  7533  7631  7729  7827  7925  8023  8121  8219
## [22261]  8317  8415  8513  8611  8709  8807  8905  9003  9101  9199  9297  9395
## [22273]  9493  9591  9689  9787  9885  9983 10081 10179 10277 10375 10473 10571
## [22285] 10669 10767 10865 10963 11061 11159 11257 11355 11453 11551 11649 11747
## [22297] 11845 11943 12041 12139 12237 12335 12433 12531 12629 12727 12825 12923
## [22309] 13021 13119 13217 13315 13413 13511 13609 13707 13805 13903 14001 14099
## [22321] 14197 14295 14393 14491 14589 14687 14785 14883 14981 15079 15177 15275
## [22333] 15373 15471 15569 15667 15765 15863 15961 16059 16157 16255 16353 16451
## [22345] 16549 16647 16745 16843 16941 17039 17137 17235 17333 17431 17529 17627
## [22357] 17725 17823 17921 18019 18117 18215 18313 18411 18509 18607 18705 18803
## [22369] 18901 18999 19097 19195 19293 19391 19489 19587 19685 19783 19881 19979
## [22381] 20077 20175 20273 20371 20469 20567 20665 20763 20861 20959 21057 21155
## [22393] 21253 21351 21449 21547 21645 21743 21841 21939 22037 22135 22233 22331
## [22405] 22429 22527 22625 22723 22821 22919 23017 23115 23213 23311 23409 23507
## [22417] 23605 23703 23801 23899 23997 24095 24193 24291 24389 24487 24585 24683
## [22429] 24781 24879 24977 25075 25173 25271 25369 25467 25565 25663 25761 25859
## [22441]    86   184   282   380   478   576   674   772   870   968  1066  1164
## [22453]  1262  1360  1458  1556  1654  1752  1850  1948  2046  2144  2242  2340
## [22465]  2438  2536  2634  2732  2830  2928  3026  3124  3222  3320  3418  3516
## [22477]  3614  3712  3810  3908  4006  4104  4202  4300  4398  4496  4594  4692
## [22489]  4790  4888  4986  5084  5182  5280  5378  5476  5574  5672  5770  5868
## [22501]  5966  6064  6162  6260  6358  6456  6554  6652  6750  6848  6946  7044
## [22513]  7142  7240  7338  7436  7534  7632  7730  7828  7926  8024  8122  8220
## [22525]  8318  8416  8514  8612  8710  8808  8906  9004  9102  9200  9298  9396
## [22537]  9494  9592  9690  9788  9886  9984 10082 10180 10278 10376 10474 10572
## [22549] 10670 10768 10866 10964 11062 11160 11258 11356 11454 11552 11650 11748
## [22561] 11846 11944 12042 12140 12238 12336 12434 12532 12630 12728 12826 12924
## [22573] 13022 13120 13218 13316 13414 13512 13610 13708 13806 13904 14002 14100
## [22585] 14198 14296 14394 14492 14590 14688 14786 14884 14982 15080 15178 15276
## [22597] 15374 15472 15570 15668 15766 15864 15962 16060 16158 16256 16354 16452
## [22609] 16550 16648 16746 16844 16942 17040 17138 17236 17334 17432 17530 17628
## [22621] 17726 17824 17922 18020 18118 18216 18314 18412 18510 18608 18706 18804
## [22633] 18902 19000 19098 19196 19294 19392 19490 19588 19686 19784 19882 19980
## [22645] 20078 20176 20274 20372 20470 20568 20666 20764 20862 20960 21058 21156
## [22657] 21254 21352 21450 21548 21646 21744 21842 21940 22038 22136 22234 22332
## [22669] 22430 22528 22626 22724 22822 22920 23018 23116 23214 23312 23410 23508
## [22681] 23606 23704 23802 23900 23998 24096 24194 24292 24390 24488 24586 24684
## [22693] 24782 24880 24978 25076 25174 25272 25370 25468 25566 25664 25762 25860
## [22705]    87   185   283   381   479   577   675   773   871   969  1067  1165
## [22717]  1263  1361  1459  1557  1655  1753  1851  1949  2047  2145  2243  2341
## [22729]  2439  2537  2635  2733  2831  2929  3027  3125  3223  3321  3419  3517
## [22741]  3615  3713  3811  3909  4007  4105  4203  4301  4399  4497  4595  4693
## [22753]  4791  4889  4987  5085  5183  5281  5379  5477  5575  5673  5771  5869
## [22765]  5967  6065  6163  6261  6359  6457  6555  6653  6751  6849  6947  7045
## [22777]  7143  7241  7339  7437  7535  7633  7731  7829  7927  8025  8123  8221
## [22789]  8319  8417  8515  8613  8711  8809  8907  9005  9103  9201  9299  9397
## [22801]  9495  9593  9691  9789  9887  9985 10083 10181 10279 10377 10475 10573
## [22813] 10671 10769 10867 10965 11063 11161 11259 11357 11455 11553 11651 11749
## [22825] 11847 11945 12043 12141 12239 12337 12435 12533 12631 12729 12827 12925
## [22837] 13023 13121 13219 13317 13415 13513 13611 13709 13807 13905 14003 14101
## [22849] 14199 14297 14395 14493 14591 14689 14787 14885 14983 15081 15179 15277
## [22861] 15375 15473 15571 15669 15767 15865 15963 16061 16159 16257 16355 16453
## [22873] 16551 16649 16747 16845 16943 17041 17139 17237 17335 17433 17531 17629
## [22885] 17727 17825 17923 18021 18119 18217 18315 18413 18511 18609 18707 18805
## [22897] 18903 19001 19099 19197 19295 19393 19491 19589 19687 19785 19883 19981
## [22909] 20079 20177 20275 20373 20471 20569 20667 20765 20863 20961 21059 21157
## [22921] 21255 21353 21451 21549 21647 21745 21843 21941 22039 22137 22235 22333
## [22933] 22431 22529 22627 22725 22823 22921 23019 23117 23215 23313 23411 23509
## [22945] 23607 23705 23803 23901 23999 24097 24195 24293 24391 24489 24587 24685
## [22957] 24783 24881 24979 25077 25175 25273 25371 25469 25567 25665 25763 25861
## [22969]    88   186   284   382   480   578   676   774   872   970  1068  1166
## [22981]  1264  1362  1460  1558  1656  1754  1852  1950  2048  2146  2244  2342
## [22993]  2440  2538  2636  2734  2832  2930  3028  3126  3224  3322  3420  3518
## [23005]  3616  3714  3812  3910  4008  4106  4204  4302  4400  4498  4596  4694
## [23017]  4792  4890  4988  5086  5184  5282  5380  5478  5576  5674  5772  5870
## [23029]  5968  6066  6164  6262  6360  6458  6556  6654  6752  6850  6948  7046
## [23041]  7144  7242  7340  7438  7536  7634  7732  7830  7928  8026  8124  8222
## [23053]  8320  8418  8516  8614  8712  8810  8908  9006  9104  9202  9300  9398
## [23065]  9496  9594  9692  9790  9888  9986 10084 10182 10280 10378 10476 10574
## [23077] 10672 10770 10868 10966 11064 11162 11260 11358 11456 11554 11652 11750
## [23089] 11848 11946 12044 12142 12240 12338 12436 12534 12632 12730 12828 12926
## [23101] 13024 13122 13220 13318 13416 13514 13612 13710 13808 13906 14004 14102
## [23113] 14200 14298 14396 14494 14592 14690 14788 14886 14984 15082 15180 15278
## [23125] 15376 15474 15572 15670 15768 15866 15964 16062 16160 16258 16356 16454
## [23137] 16552 16650 16748 16846 16944 17042 17140 17238 17336 17434 17532 17630
## [23149] 17728 17826 17924 18022 18120 18218 18316 18414 18512 18610 18708 18806
## [23161] 18904 19002 19100 19198 19296 19394 19492 19590 19688 19786 19884 19982
## [23173] 20080 20178 20276 20374 20472 20570 20668 20766 20864 20962 21060 21158
## [23185] 21256 21354 21452 21550 21648 21746 21844 21942 22040 22138 22236 22334
## [23197] 22432 22530 22628 22726 22824 22922 23020 23118 23216 23314 23412 23510
## [23209] 23608 23706 23804 23902 24000 24098 24196 24294 24392 24490 24588 24686
## [23221] 24784 24882 24980 25078 25176 25274 25372 25470 25568 25666 25764 25862
## [23233]    89   187   285   383   481   579   677   775   873   971  1069  1167
## [23245]  1265  1363  1461  1559  1657  1755  1853  1951  2049  2147  2245  2343
## [23257]  2441  2539  2637  2735  2833  2931  3029  3127  3225  3323  3421  3519
## [23269]  3617  3715  3813  3911  4009  4107  4205  4303  4401  4499  4597  4695
## [23281]  4793  4891  4989  5087  5185  5283  5381  5479  5577  5675  5773  5871
## [23293]  5969  6067  6165  6263  6361  6459  6557  6655  6753  6851  6949  7047
## [23305]  7145  7243  7341  7439  7537  7635  7733  7831  7929  8027  8125  8223
## [23317]  8321  8419  8517  8615  8713  8811  8909  9007  9105  9203  9301  9399
## [23329]  9497  9595  9693  9791  9889  9987 10085 10183 10281 10379 10477 10575
## [23341] 10673 10771 10869 10967 11065 11163 11261 11359 11457 11555 11653 11751
## [23353] 11849 11947 12045 12143 12241 12339 12437 12535 12633 12731 12829 12927
## [23365] 13025 13123 13221 13319 13417 13515 13613 13711 13809 13907 14005 14103
## [23377] 14201 14299 14397 14495 14593 14691 14789 14887 14985 15083 15181 15279
## [23389] 15377 15475 15573 15671 15769 15867 15965 16063 16161 16259 16357 16455
## [23401] 16553 16651 16749 16847 16945 17043 17141 17239 17337 17435 17533 17631
## [23413] 17729 17827 17925 18023 18121 18219 18317 18415 18513 18611 18709 18807
## [23425] 18905 19003 19101 19199 19297 19395 19493 19591 19689 19787 19885 19983
## [23437] 20081 20179 20277 20375 20473 20571 20669 20767 20865 20963 21061 21159
## [23449] 21257 21355 21453 21551 21649 21747 21845 21943 22041 22139 22237 22335
## [23461] 22433 22531 22629 22727 22825 22923 23021 23119 23217 23315 23413 23511
## [23473] 23609 23707 23805 23903 24001 24099 24197 24295 24393 24491 24589 24687
## [23485] 24785 24883 24981 25079 25177 25275 25373 25471 25569 25667 25765 25863
## [23497]    90   188   286   384   482   580   678   776   874   972  1070  1168
## [23509]  1266  1364  1462  1560  1658  1756  1854  1952  2050  2148  2246  2344
## [23521]  2442  2540  2638  2736  2834  2932  3030  3128  3226  3324  3422  3520
## [23533]  3618  3716  3814  3912  4010  4108  4206  4304  4402  4500  4598  4696
## [23545]  4794  4892  4990  5088  5186  5284  5382  5480  5578  5676  5774  5872
## [23557]  5970  6068  6166  6264  6362  6460  6558  6656  6754  6852  6950  7048
## [23569]  7146  7244  7342  7440  7538  7636  7734  7832  7930  8028  8126  8224
## [23581]  8322  8420  8518  8616  8714  8812  8910  9008  9106  9204  9302  9400
## [23593]  9498  9596  9694  9792  9890  9988 10086 10184 10282 10380 10478 10576
## [23605] 10674 10772 10870 10968 11066 11164 11262 11360 11458 11556 11654 11752
## [23617] 11850 11948 12046 12144 12242 12340 12438 12536 12634 12732 12830 12928
## [23629] 13026 13124 13222 13320 13418 13516 13614 13712 13810 13908 14006 14104
## [23641] 14202 14300 14398 14496 14594 14692 14790 14888 14986 15084 15182 15280
## [23653] 15378 15476 15574 15672 15770 15868 15966 16064 16162 16260 16358 16456
## [23665] 16554 16652 16750 16848 16946 17044 17142 17240 17338 17436 17534 17632
## [23677] 17730 17828 17926 18024 18122 18220 18318 18416 18514 18612 18710 18808
## [23689] 18906 19004 19102 19200 19298 19396 19494 19592 19690 19788 19886 19984
## [23701] 20082 20180 20278 20376 20474 20572 20670 20768 20866 20964 21062 21160
## [23713] 21258 21356 21454 21552 21650 21748 21846 21944 22042 22140 22238 22336
## [23725] 22434 22532 22630 22728 22826 22924 23022 23120 23218 23316 23414 23512
## [23737] 23610 23708 23806 23904 24002 24100 24198 24296 24394 24492 24590 24688
## [23749] 24786 24884 24982 25080 25178 25276 25374 25472 25570 25668 25766 25864
## [23761]    91   189   287   385   483   581   679   777   875   973  1071  1169
## [23773]  1267  1365  1463  1561  1659  1757  1855  1953  2051  2149  2247  2345
## [23785]  2443  2541  2639  2737  2835  2933  3031  3129  3227  3325  3423  3521
## [23797]  3619  3717  3815  3913  4011  4109  4207  4305  4403  4501  4599  4697
## [23809]  4795  4893  4991  5089  5187  5285  5383  5481  5579  5677  5775  5873
## [23821]  5971  6069  6167  6265  6363  6461  6559  6657  6755  6853  6951  7049
## [23833]  7147  7245  7343  7441  7539  7637  7735  7833  7931  8029  8127  8225
## [23845]  8323  8421  8519  8617  8715  8813  8911  9009  9107  9205  9303  9401
## [23857]  9499  9597  9695  9793  9891  9989 10087 10185 10283 10381 10479 10577
## [23869] 10675 10773 10871 10969 11067 11165 11263 11361 11459 11557 11655 11753
## [23881] 11851 11949 12047 12145 12243 12341 12439 12537 12635 12733 12831 12929
## [23893] 13027 13125 13223 13321 13419 13517 13615 13713 13811 13909 14007 14105
## [23905] 14203 14301 14399 14497 14595 14693 14791 14889 14987 15085 15183 15281
## [23917] 15379 15477 15575 15673 15771 15869 15967 16065 16163 16261 16359 16457
## [23929] 16555 16653 16751 16849 16947 17045 17143 17241 17339 17437 17535 17633
## [23941] 17731 17829 17927 18025 18123 18221 18319 18417 18515 18613 18711 18809
## [23953] 18907 19005 19103 19201 19299 19397 19495 19593 19691 19789 19887 19985
## [23965] 20083 20181 20279 20377 20475 20573 20671 20769 20867 20965 21063 21161
## [23977] 21259 21357 21455 21553 21651 21749 21847 21945 22043 22141 22239 22337
## [23989] 22435 22533 22631 22729 22827 22925 23023 23121 23219 23317 23415 23513
## [24001] 23611 23709 23807 23905 24003 24101 24199 24297 24395 24493 24591 24689
## [24013] 24787 24885 24983 25081 25179 25277 25375 25473 25571 25669 25767 25865
## [24025]    92   190   288   386   484   582   680   778   876   974  1072  1170
## [24037]  1268  1366  1464  1562  1660  1758  1856  1954  2052  2150  2248  2346
## [24049]  2444  2542  2640  2738  2836  2934  3032  3130  3228  3326  3424  3522
## [24061]  3620  3718  3816  3914  4012  4110  4208  4306  4404  4502  4600  4698
## [24073]  4796  4894  4992  5090  5188  5286  5384  5482  5580  5678  5776  5874
## [24085]  5972  6070  6168  6266  6364  6462  6560  6658  6756  6854  6952  7050
## [24097]  7148  7246  7344  7442  7540  7638  7736  7834  7932  8030  8128  8226
## [24109]  8324  8422  8520  8618  8716  8814  8912  9010  9108  9206  9304  9402
## [24121]  9500  9598  9696  9794  9892  9990 10088 10186 10284 10382 10480 10578
## [24133] 10676 10774 10872 10970 11068 11166 11264 11362 11460 11558 11656 11754
## [24145] 11852 11950 12048 12146 12244 12342 12440 12538 12636 12734 12832 12930
## [24157] 13028 13126 13224 13322 13420 13518 13616 13714 13812 13910 14008 14106
## [24169] 14204 14302 14400 14498 14596 14694 14792 14890 14988 15086 15184 15282
## [24181] 15380 15478 15576 15674 15772 15870 15968 16066 16164 16262 16360 16458
## [24193] 16556 16654 16752 16850 16948 17046 17144 17242 17340 17438 17536 17634
## [24205] 17732 17830 17928 18026 18124 18222 18320 18418 18516 18614 18712 18810
## [24217] 18908 19006 19104 19202 19300 19398 19496 19594 19692 19790 19888 19986
## [24229] 20084 20182 20280 20378 20476 20574 20672 20770 20868 20966 21064 21162
## [24241] 21260 21358 21456 21554 21652 21750 21848 21946 22044 22142 22240 22338
## [24253] 22436 22534 22632 22730 22828 22926 23024 23122 23220 23318 23416 23514
## [24265] 23612 23710 23808 23906 24004 24102 24200 24298 24396 24494 24592 24690
## [24277] 24788 24886 24984 25082 25180 25278 25376 25474 25572 25670 25768 25866
## [24289]    93   191   289   387   485   583   681   779   877   975  1073  1171
## [24301]  1269  1367  1465  1563  1661  1759  1857  1955  2053  2151  2249  2347
## [24313]  2445  2543  2641  2739  2837  2935  3033  3131  3229  3327  3425  3523
## [24325]  3621  3719  3817  3915  4013  4111  4209  4307  4405  4503  4601  4699
## [24337]  4797  4895  4993  5091  5189  5287  5385  5483  5581  5679  5777  5875
## [24349]  5973  6071  6169  6267  6365  6463  6561  6659  6757  6855  6953  7051
## [24361]  7149  7247  7345  7443  7541  7639  7737  7835  7933  8031  8129  8227
## [24373]  8325  8423  8521  8619  8717  8815  8913  9011  9109  9207  9305  9403
## [24385]  9501  9599  9697  9795  9893  9991 10089 10187 10285 10383 10481 10579
## [24397] 10677 10775 10873 10971 11069 11167 11265 11363 11461 11559 11657 11755
## [24409] 11853 11951 12049 12147 12245 12343 12441 12539 12637 12735 12833 12931
## [24421] 13029 13127 13225 13323 13421 13519 13617 13715 13813 13911 14009 14107
## [24433] 14205 14303 14401 14499 14597 14695 14793 14891 14989 15087 15185 15283
## [24445] 15381 15479 15577 15675 15773 15871 15969 16067 16165 16263 16361 16459
## [24457] 16557 16655 16753 16851 16949 17047 17145 17243 17341 17439 17537 17635
## [24469] 17733 17831 17929 18027 18125 18223 18321 18419 18517 18615 18713 18811
## [24481] 18909 19007 19105 19203 19301 19399 19497 19595 19693 19791 19889 19987
## [24493] 20085 20183 20281 20379 20477 20575 20673 20771 20869 20967 21065 21163
## [24505] 21261 21359 21457 21555 21653 21751 21849 21947 22045 22143 22241 22339
## [24517] 22437 22535 22633 22731 22829 22927 23025 23123 23221 23319 23417 23515
## [24529] 23613 23711 23809 23907 24005 24103 24201 24299 24397 24495 24593 24691
## [24541] 24789 24887 24985 25083 25181 25279 25377 25475 25573 25671 25769 25867
## [24553]    94   192   290   388   486   584   682   780   878   976  1074  1172
## [24565]  1270  1368  1466  1564  1662  1760  1858  1956  2054  2152  2250  2348
## [24577]  2446  2544  2642  2740  2838  2936  3034  3132  3230  3328  3426  3524
## [24589]  3622  3720  3818  3916  4014  4112  4210  4308  4406  4504  4602  4700
## [24601]  4798  4896  4994  5092  5190  5288  5386  5484  5582  5680  5778  5876
## [24613]  5974  6072  6170  6268  6366  6464  6562  6660  6758  6856  6954  7052
## [24625]  7150  7248  7346  7444  7542  7640  7738  7836  7934  8032  8130  8228
## [24637]  8326  8424  8522  8620  8718  8816  8914  9012  9110  9208  9306  9404
## [24649]  9502  9600  9698  9796  9894  9992 10090 10188 10286 10384 10482 10580
## [24661] 10678 10776 10874 10972 11070 11168 11266 11364 11462 11560 11658 11756
## [24673] 11854 11952 12050 12148 12246 12344 12442 12540 12638 12736 12834 12932
## [24685] 13030 13128 13226 13324 13422 13520 13618 13716 13814 13912 14010 14108
## [24697] 14206 14304 14402 14500 14598 14696 14794 14892 14990 15088 15186 15284
## [24709] 15382 15480 15578 15676 15774 15872 15970 16068 16166 16264 16362 16460
## [24721] 16558 16656 16754 16852 16950 17048 17146 17244 17342 17440 17538 17636
## [24733] 17734 17832 17930 18028 18126 18224 18322 18420 18518 18616 18714 18812
## [24745] 18910 19008 19106 19204 19302 19400 19498 19596 19694 19792 19890 19988
## [24757] 20086 20184 20282 20380 20478 20576 20674 20772 20870 20968 21066 21164
## [24769] 21262 21360 21458 21556 21654 21752 21850 21948 22046 22144 22242 22340
## [24781] 22438 22536 22634 22732 22830 22928 23026 23124 23222 23320 23418 23516
## [24793] 23614 23712 23810 23908 24006 24104 24202 24300 24398 24496 24594 24692
## [24805] 24790 24888 24986 25084 25182 25280 25378 25476 25574 25672 25770 25868
## [24817]    95   193   291   389   487   585   683   781   879   977  1075  1173
## [24829]  1271  1369  1467  1565  1663  1761  1859  1957  2055  2153  2251  2349
## [24841]  2447  2545  2643  2741  2839  2937  3035  3133  3231  3329  3427  3525
## [24853]  3623  3721  3819  3917  4015  4113  4211  4309  4407  4505  4603  4701
## [24865]  4799  4897  4995  5093  5191  5289  5387  5485  5583  5681  5779  5877
## [24877]  5975  6073  6171  6269  6367  6465  6563  6661  6759  6857  6955  7053
## [24889]  7151  7249  7347  7445  7543  7641  7739  7837  7935  8033  8131  8229
## [24901]  8327  8425  8523  8621  8719  8817  8915  9013  9111  9209  9307  9405
## [24913]  9503  9601  9699  9797  9895  9993 10091 10189 10287 10385 10483 10581
## [24925] 10679 10777 10875 10973 11071 11169 11267 11365 11463 11561 11659 11757
## [24937] 11855 11953 12051 12149 12247 12345 12443 12541 12639 12737 12835 12933
## [24949] 13031 13129 13227 13325 13423 13521 13619 13717 13815 13913 14011 14109
## [24961] 14207 14305 14403 14501 14599 14697 14795 14893 14991 15089 15187 15285
## [24973] 15383 15481 15579 15677 15775 15873 15971 16069 16167 16265 16363 16461
## [24985] 16559 16657 16755 16853 16951 17049 17147 17245 17343 17441 17539 17637
## [24997] 17735 17833 17931 18029 18127 18225 18323 18421 18519 18617 18715 18813
## [25009] 18911 19009 19107 19205 19303 19401 19499 19597 19695 19793 19891 19989
## [25021] 20087 20185 20283 20381 20479 20577 20675 20773 20871 20969 21067 21165
## [25033] 21263 21361 21459 21557 21655 21753 21851 21949 22047 22145 22243 22341
## [25045] 22439 22537 22635 22733 22831 22929 23027 23125 23223 23321 23419 23517
## [25057] 23615 23713 23811 23909 24007 24105 24203 24301 24399 24497 24595 24693
## [25069] 24791 24889 24987 25085 25183 25281 25379 25477 25575 25673 25771 25869
## [25081]    96   194   292   390   488   586   684   782   880   978  1076  1174
## [25093]  1272  1370  1468  1566  1664  1762  1860  1958  2056  2154  2252  2350
## [25105]  2448  2546  2644  2742  2840  2938  3036  3134  3232  3330  3428  3526
## [25117]  3624  3722  3820  3918  4016  4114  4212  4310  4408  4506  4604  4702
## [25129]  4800  4898  4996  5094  5192  5290  5388  5486  5584  5682  5780  5878
## [25141]  5976  6074  6172  6270  6368  6466  6564  6662  6760  6858  6956  7054
## [25153]  7152  7250  7348  7446  7544  7642  7740  7838  7936  8034  8132  8230
## [25165]  8328  8426  8524  8622  8720  8818  8916  9014  9112  9210  9308  9406
## [25177]  9504  9602  9700  9798  9896  9994 10092 10190 10288 10386 10484 10582
## [25189] 10680 10778 10876 10974 11072 11170 11268 11366 11464 11562 11660 11758
## [25201] 11856 11954 12052 12150 12248 12346 12444 12542 12640 12738 12836 12934
## [25213] 13032 13130 13228 13326 13424 13522 13620 13718 13816 13914 14012 14110
## [25225] 14208 14306 14404 14502 14600 14698 14796 14894 14992 15090 15188 15286
## [25237] 15384 15482 15580 15678 15776 15874 15972 16070 16168 16266 16364 16462
## [25249] 16560 16658 16756 16854 16952 17050 17148 17246 17344 17442 17540 17638
## [25261] 17736 17834 17932 18030 18128 18226 18324 18422 18520 18618 18716 18814
## [25273] 18912 19010 19108 19206 19304 19402 19500 19598 19696 19794 19892 19990
## [25285] 20088 20186 20284 20382 20480 20578 20676 20774 20872 20970 21068 21166
## [25297] 21264 21362 21460 21558 21656 21754 21852 21950 22048 22146 22244 22342
## [25309] 22440 22538 22636 22734 22832 22930 23028 23126 23224 23322 23420 23518
## [25321] 23616 23714 23812 23910 24008 24106 24204 24302 24400 24498 24596 24694
## [25333] 24792 24890 24988 25086 25184 25282 25380 25478 25576 25674 25772 25870
## [25345]    97   195   293   391   489   587   685   783   881   979  1077  1175
## [25357]  1273  1371  1469  1567  1665  1763  1861  1959  2057  2155  2253  2351
## [25369]  2449  2547  2645  2743  2841  2939  3037  3135  3233  3331  3429  3527
## [25381]  3625  3723  3821  3919  4017  4115  4213  4311  4409  4507  4605  4703
## [25393]  4801  4899  4997  5095  5193  5291  5389  5487  5585  5683  5781  5879
## [25405]  5977  6075  6173  6271  6369  6467  6565  6663  6761  6859  6957  7055
## [25417]  7153  7251  7349  7447  7545  7643  7741  7839  7937  8035  8133  8231
## [25429]  8329  8427  8525  8623  8721  8819  8917  9015  9113  9211  9309  9407
## [25441]  9505  9603  9701  9799  9897  9995 10093 10191 10289 10387 10485 10583
## [25453] 10681 10779 10877 10975 11073 11171 11269 11367 11465 11563 11661 11759
## [25465] 11857 11955 12053 12151 12249 12347 12445 12543 12641 12739 12837 12935
## [25477] 13033 13131 13229 13327 13425 13523 13621 13719 13817 13915 14013 14111
## [25489] 14209 14307 14405 14503 14601 14699 14797 14895 14993 15091 15189 15287
## [25501] 15385 15483 15581 15679 15777 15875 15973 16071 16169 16267 16365 16463
## [25513] 16561 16659 16757 16855 16953 17051 17149 17247 17345 17443 17541 17639
## [25525] 17737 17835 17933 18031 18129 18227 18325 18423 18521 18619 18717 18815
## [25537] 18913 19011 19109 19207 19305 19403 19501 19599 19697 19795 19893 19991
## [25549] 20089 20187 20285 20383 20481 20579 20677 20775 20873 20971 21069 21167
## [25561] 21265 21363 21461 21559 21657 21755 21853 21951 22049 22147 22245 22343
## [25573] 22441 22539 22637 22735 22833 22931 23029 23127 23225 23323 23421 23519
## [25585] 23617 23715 23813 23911 24009 24107 24205 24303 24401 24499 24597 24695
## [25597] 24793 24891 24989 25087 25185 25283 25381 25479 25577 25675 25773 25871
## [25609]    98   196   294   392   490   588   686   784   882   980  1078  1176
## [25621]  1274  1372  1470  1568  1666  1764  1862  1960  2058  2156  2254  2352
## [25633]  2450  2548  2646  2744  2842  2940  3038  3136  3234  3332  3430  3528
## [25645]  3626  3724  3822  3920  4018  4116  4214  4312  4410  4508  4606  4704
## [25657]  4802  4900  4998  5096  5194  5292  5390  5488  5586  5684  5782  5880
## [25669]  5978  6076  6174  6272  6370  6468  6566  6664  6762  6860  6958  7056
## [25681]  7154  7252  7350  7448  7546  7644  7742  7840  7938  8036  8134  8232
## [25693]  8330  8428  8526  8624  8722  8820  8918  9016  9114  9212  9310  9408
## [25705]  9506  9604  9702  9800  9898  9996 10094 10192 10290 10388 10486 10584
## [25717] 10682 10780 10878 10976 11074 11172 11270 11368 11466 11564 11662 11760
## [25729] 11858 11956 12054 12152 12250 12348 12446 12544 12642 12740 12838 12936
## [25741] 13034 13132 13230 13328 13426 13524 13622 13720 13818 13916 14014 14112
## [25753] 14210 14308 14406 14504 14602 14700 14798 14896 14994 15092 15190 15288
## [25765] 15386 15484 15582 15680 15778 15876 15974 16072 16170 16268 16366 16464
## [25777] 16562 16660 16758 16856 16954 17052 17150 17248 17346 17444 17542 17640
## [25789] 17738 17836 17934 18032 18130 18228 18326 18424 18522 18620 18718 18816
## [25801] 18914 19012 19110 19208 19306 19404 19502 19600 19698 19796 19894 19992
## [25813] 20090 20188 20286 20384 20482 20580 20678 20776 20874 20972 21070 21168
## [25825] 21266 21364 21462 21560 21658 21756 21854 21952 22050 22148 22246 22344
## [25837] 22442 22540 22638 22736 22834 22932 23030 23128 23226 23324 23422 23520
## [25849] 23618 23716 23814 23912 24010 24108 24206 24304 24402 24500 24598 24696
## [25861] 24794 24892 24990 25088 25186 25284 25382 25480 25578 25676 25774 25872

# Now, I repeat these steps for the deceased data. 
Initial_Deceased_Wide <- read_csv("data/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   `Province/State` = col_character(),
##   `Country/Region` = col_character()
## )

## See spec(...) for full column specifications.

# We continue with the reshape.
Deceased_Long <- pivot_longer(Initial_Deceased_Wide, cols = c(5:102), names_to = "Dates", values_to = "Deceased")
require(reshape)
Deceased_Long$COUNTRY <- Deceased_Long$`Country/Region`
Deceased_Long$Date <- mdy(Deceased_Long$Dates, quiet = FALSE, tz = NULL, locale = Sys.getlocale("LC_TIME"),
  truncated = 0)
order(Deceased_Long$Date)

##     [1]     1    99   197   295   393   491   589   687   785   883   981  1079
##    [13]  1177  1275  1373  1471  1569  1667  1765  1863  1961  2059  2157  2255
##    [25]  2353  2451  2549  2647  2745  2843  2941  3039  3137  3235  3333  3431
##    [37]  3529  3627  3725  3823  3921  4019  4117  4215  4313  4411  4509  4607
##    [49]  4705  4803  4901  4999  5097  5195  5293  5391  5489  5587  5685  5783
##    [61]  5881  5979  6077  6175  6273  6371  6469  6567  6665  6763  6861  6959
##    [73]  7057  7155  7253  7351  7449  7547  7645  7743  7841  7939  8037  8135
##    [85]  8233  8331  8429  8527  8625  8723  8821  8919  9017  9115  9213  9311
##    [97]  9409  9507  9605  9703  9801  9899  9997 10095 10193 10291 10389 10487
##   [109] 10585 10683 10781 10879 10977 11075 11173 11271 11369 11467 11565 11663
##   [121] 11761 11859 11957 12055 12153 12251 12349 12447 12545 12643 12741 12839
##   [133] 12937 13035 13133 13231 13329 13427 13525 13623 13721 13819 13917 14015
##   [145] 14113 14211 14309 14407 14505 14603 14701 14799 14897 14995 15093 15191
##   [157] 15289 15387 15485 15583 15681 15779 15877 15975 16073 16171 16269 16367
##   [169] 16465 16563 16661 16759 16857 16955 17053 17151 17249 17347 17445 17543
##   [181] 17641 17739 17837 17935 18033 18131 18229 18327 18425 18523 18621 18719
##   [193] 18817 18915 19013 19111 19209 19307 19405 19503 19601 19699 19797 19895
##   [205] 19993 20091 20189 20287 20385 20483 20581 20679 20777 20875 20973 21071
##   [217] 21169 21267 21365 21463 21561 21659 21757 21855 21953 22051 22149 22247
##   [229] 22345 22443 22541 22639 22737 22835 22933 23031 23129 23227 23325 23423
##   [241] 23521 23619 23717 23815 23913 24011 24109 24207 24305 24403 24501 24599
##   [253] 24697 24795 24893 24991 25089 25187 25285 25383 25481 25579 25677 25775
##   [265]     2   100   198   296   394   492   590   688   786   884   982  1080
##   [277]  1178  1276  1374  1472  1570  1668  1766  1864  1962  2060  2158  2256
##   [289]  2354  2452  2550  2648  2746  2844  2942  3040  3138  3236  3334  3432
##   [301]  3530  3628  3726  3824  3922  4020  4118  4216  4314  4412  4510  4608
##   [313]  4706  4804  4902  5000  5098  5196  5294  5392  5490  5588  5686  5784
##   [325]  5882  5980  6078  6176  6274  6372  6470  6568  6666  6764  6862  6960
##   [337]  7058  7156  7254  7352  7450  7548  7646  7744  7842  7940  8038  8136
##   [349]  8234  8332  8430  8528  8626  8724  8822  8920  9018  9116  9214  9312
##   [361]  9410  9508  9606  9704  9802  9900  9998 10096 10194 10292 10390 10488
##   [373] 10586 10684 10782 10880 10978 11076 11174 11272 11370 11468 11566 11664
##   [385] 11762 11860 11958 12056 12154 12252 12350 12448 12546 12644 12742 12840
##   [397] 12938 13036 13134 13232 13330 13428 13526 13624 13722 13820 13918 14016
##   [409] 14114 14212 14310 14408 14506 14604 14702 14800 14898 14996 15094 15192
##   [421] 15290 15388 15486 15584 15682 15780 15878 15976 16074 16172 16270 16368
##   [433] 16466 16564 16662 16760 16858 16956 17054 17152 17250 17348 17446 17544
##   [445] 17642 17740 17838 17936 18034 18132 18230 18328 18426 18524 18622 18720
##   [457] 18818 18916 19014 19112 19210 19308 19406 19504 19602 19700 19798 19896
##   [469] 19994 20092 20190 20288 20386 20484 20582 20680 20778 20876 20974 21072
##   [481] 21170 21268 21366 21464 21562 21660 21758 21856 21954 22052 22150 22248
##   [493] 22346 22444 22542 22640 22738 22836 22934 23032 23130 23228 23326 23424
##   [505] 23522 23620 23718 23816 23914 24012 24110 24208 24306 24404 24502 24600
##   [517] 24698 24796 24894 24992 25090 25188 25286 25384 25482 25580 25678 25776
##   [529]     3   101   199   297   395   493   591   689   787   885   983  1081
##   [541]  1179  1277  1375  1473  1571  1669  1767  1865  1963  2061  2159  2257
##   [553]  2355  2453  2551  2649  2747  2845  2943  3041  3139  3237  3335  3433
##   [565]  3531  3629  3727  3825  3923  4021  4119  4217  4315  4413  4511  4609
##   [577]  4707  4805  4903  5001  5099  5197  5295  5393  5491  5589  5687  5785
##   [589]  5883  5981  6079  6177  6275  6373  6471  6569  6667  6765  6863  6961
##   [601]  7059  7157  7255  7353  7451  7549  7647  7745  7843  7941  8039  8137
##   [613]  8235  8333  8431  8529  8627  8725  8823  8921  9019  9117  9215  9313
##   [625]  9411  9509  9607  9705  9803  9901  9999 10097 10195 10293 10391 10489
##   [637] 10587 10685 10783 10881 10979 11077 11175 11273 11371 11469 11567 11665
##   [649] 11763 11861 11959 12057 12155 12253 12351 12449 12547 12645 12743 12841
##   [661] 12939 13037 13135 13233 13331 13429 13527 13625 13723 13821 13919 14017
##   [673] 14115 14213 14311 14409 14507 14605 14703 14801 14899 14997 15095 15193
##   [685] 15291 15389 15487 15585 15683 15781 15879 15977 16075 16173 16271 16369
##   [697] 16467 16565 16663 16761 16859 16957 17055 17153 17251 17349 17447 17545
##   [709] 17643 17741 17839 17937 18035 18133 18231 18329 18427 18525 18623 18721
##   [721] 18819 18917 19015 19113 19211 19309 19407 19505 19603 19701 19799 19897
##   [733] 19995 20093 20191 20289 20387 20485 20583 20681 20779 20877 20975 21073
##   [745] 21171 21269 21367 21465 21563 21661 21759 21857 21955 22053 22151 22249
##   [757] 22347 22445 22543 22641 22739 22837 22935 23033 23131 23229 23327 23425
##   [769] 23523 23621 23719 23817 23915 24013 24111 24209 24307 24405 24503 24601
##   [781] 24699 24797 24895 24993 25091 25189 25287 25385 25483 25581 25679 25777
##   [793]     4   102   200   298   396   494   592   690   788   886   984  1082
##   [805]  1180  1278  1376  1474  1572  1670  1768  1866  1964  2062  2160  2258
##   [817]  2356  2454  2552  2650  2748  2846  2944  3042  3140  3238  3336  3434
##   [829]  3532  3630  3728  3826  3924  4022  4120  4218  4316  4414  4512  4610
##   [841]  4708  4806  4904  5002  5100  5198  5296  5394  5492  5590  5688  5786
##   [853]  5884  5982  6080  6178  6276  6374  6472  6570  6668  6766  6864  6962
##   [865]  7060  7158  7256  7354  7452  7550  7648  7746  7844  7942  8040  8138
##   [877]  8236  8334  8432  8530  8628  8726  8824  8922  9020  9118  9216  9314
##   [889]  9412  9510  9608  9706  9804  9902 10000 10098 10196 10294 10392 10490
##   [901] 10588 10686 10784 10882 10980 11078 11176 11274 11372 11470 11568 11666
##   [913] 11764 11862 11960 12058 12156 12254 12352 12450 12548 12646 12744 12842
##   [925] 12940 13038 13136 13234 13332 13430 13528 13626 13724 13822 13920 14018
##   [937] 14116 14214 14312 14410 14508 14606 14704 14802 14900 14998 15096 15194
##   [949] 15292 15390 15488 15586 15684 15782 15880 15978 16076 16174 16272 16370
##   [961] 16468 16566 16664 16762 16860 16958 17056 17154 17252 17350 17448 17546
##   [973] 17644 17742 17840 17938 18036 18134 18232 18330 18428 18526 18624 18722
##   [985] 18820 18918 19016 19114 19212 19310 19408 19506 19604 19702 19800 19898
##   [997] 19996 20094 20192 20290 20388 20486 20584 20682 20780 20878 20976 21074
##  [1009] 21172 21270 21368 21466 21564 21662 21760 21858 21956 22054 22152 22250
##  [1021] 22348 22446 22544 22642 22740 22838 22936 23034 23132 23230 23328 23426
##  [1033] 23524 23622 23720 23818 23916 24014 24112 24210 24308 24406 24504 24602
##  [1045] 24700 24798 24896 24994 25092 25190 25288 25386 25484 25582 25680 25778
##  [1057]     5   103   201   299   397   495   593   691   789   887   985  1083
##  [1069]  1181  1279  1377  1475  1573  1671  1769  1867  1965  2063  2161  2259
##  [1081]  2357  2455  2553  2651  2749  2847  2945  3043  3141  3239  3337  3435
##  [1093]  3533  3631  3729  3827  3925  4023  4121  4219  4317  4415  4513  4611
##  [1105]  4709  4807  4905  5003  5101  5199  5297  5395  5493  5591  5689  5787
##  [1117]  5885  5983  6081  6179  6277  6375  6473  6571  6669  6767  6865  6963
##  [1129]  7061  7159  7257  7355  7453  7551  7649  7747  7845  7943  8041  8139
##  [1141]  8237  8335  8433  8531  8629  8727  8825  8923  9021  9119  9217  9315
##  [1153]  9413  9511  9609  9707  9805  9903 10001 10099 10197 10295 10393 10491
##  [1165] 10589 10687 10785 10883 10981 11079 11177 11275 11373 11471 11569 11667
##  [1177] 11765 11863 11961 12059 12157 12255 12353 12451 12549 12647 12745 12843
##  [1189] 12941 13039 13137 13235 13333 13431 13529 13627 13725 13823 13921 14019
##  [1201] 14117 14215 14313 14411 14509 14607 14705 14803 14901 14999 15097 15195
##  [1213] 15293 15391 15489 15587 15685 15783 15881 15979 16077 16175 16273 16371
##  [1225] 16469 16567 16665 16763 16861 16959 17057 17155 17253 17351 17449 17547
##  [1237] 17645 17743 17841 17939 18037 18135 18233 18331 18429 18527 18625 18723
##  [1249] 18821 18919 19017 19115 19213 19311 19409 19507 19605 19703 19801 19899
##  [1261] 19997 20095 20193 20291 20389 20487 20585 20683 20781 20879 20977 21075
##  [1273] 21173 21271 21369 21467 21565 21663 21761 21859 21957 22055 22153 22251
##  [1285] 22349 22447 22545 22643 22741 22839 22937 23035 23133 23231 23329 23427
##  [1297] 23525 23623 23721 23819 23917 24015 24113 24211 24309 24407 24505 24603
##  [1309] 24701 24799 24897 24995 25093 25191 25289 25387 25485 25583 25681 25779
##  [1321]     6   104   202   300   398   496   594   692   790   888   986  1084
##  [1333]  1182  1280  1378  1476  1574  1672  1770  1868  1966  2064  2162  2260
##  [1345]  2358  2456  2554  2652  2750  2848  2946  3044  3142  3240  3338  3436
##  [1357]  3534  3632  3730  3828  3926  4024  4122  4220  4318  4416  4514  4612
##  [1369]  4710  4808  4906  5004  5102  5200  5298  5396  5494  5592  5690  5788
##  [1381]  5886  5984  6082  6180  6278  6376  6474  6572  6670  6768  6866  6964
##  [1393]  7062  7160  7258  7356  7454  7552  7650  7748  7846  7944  8042  8140
##  [1405]  8238  8336  8434  8532  8630  8728  8826  8924  9022  9120  9218  9316
##  [1417]  9414  9512  9610  9708  9806  9904 10002 10100 10198 10296 10394 10492
##  [1429] 10590 10688 10786 10884 10982 11080 11178 11276 11374 11472 11570 11668
##  [1441] 11766 11864 11962 12060 12158 12256 12354 12452 12550 12648 12746 12844
##  [1453] 12942 13040 13138 13236 13334 13432 13530 13628 13726 13824 13922 14020
##  [1465] 14118 14216 14314 14412 14510 14608 14706 14804 14902 15000 15098 15196
##  [1477] 15294 15392 15490 15588 15686 15784 15882 15980 16078 16176 16274 16372
##  [1489] 16470 16568 16666 16764 16862 16960 17058 17156 17254 17352 17450 17548
##  [1501] 17646 17744 17842 17940 18038 18136 18234 18332 18430 18528 18626 18724
##  [1513] 18822 18920 19018 19116 19214 19312 19410 19508 19606 19704 19802 19900
##  [1525] 19998 20096 20194 20292 20390 20488 20586 20684 20782 20880 20978 21076
##  [1537] 21174 21272 21370 21468 21566 21664 21762 21860 21958 22056 22154 22252
##  [1549] 22350 22448 22546 22644 22742 22840 22938 23036 23134 23232 23330 23428
##  [1561] 23526 23624 23722 23820 23918 24016 24114 24212 24310 24408 24506 24604
##  [1573] 24702 24800 24898 24996 25094 25192 25290 25388 25486 25584 25682 25780
##  [1585]     7   105   203   301   399   497   595   693   791   889   987  1085
##  [1597]  1183  1281  1379  1477  1575  1673  1771  1869  1967  2065  2163  2261
##  [1609]  2359  2457  2555  2653  2751  2849  2947  3045  3143  3241  3339  3437
##  [1621]  3535  3633  3731  3829  3927  4025  4123  4221  4319  4417  4515  4613
##  [1633]  4711  4809  4907  5005  5103  5201  5299  5397  5495  5593  5691  5789
##  [1645]  5887  5985  6083  6181  6279  6377  6475  6573  6671  6769  6867  6965
##  [1657]  7063  7161  7259  7357  7455  7553  7651  7749  7847  7945  8043  8141
##  [1669]  8239  8337  8435  8533  8631  8729  8827  8925  9023  9121  9219  9317
##  [1681]  9415  9513  9611  9709  9807  9905 10003 10101 10199 10297 10395 10493
##  [1693] 10591 10689 10787 10885 10983 11081 11179 11277 11375 11473 11571 11669
##  [1705] 11767 11865 11963 12061 12159 12257 12355 12453 12551 12649 12747 12845
##  [1717] 12943 13041 13139 13237 13335 13433 13531 13629 13727 13825 13923 14021
##  [1729] 14119 14217 14315 14413 14511 14609 14707 14805 14903 15001 15099 15197
##  [1741] 15295 15393 15491 15589 15687 15785 15883 15981 16079 16177 16275 16373
##  [1753] 16471 16569 16667 16765 16863 16961 17059 17157 17255 17353 17451 17549
##  [1765] 17647 17745 17843 17941 18039 18137 18235 18333 18431 18529 18627 18725
##  [1777] 18823 18921 19019 19117 19215 19313 19411 19509 19607 19705 19803 19901
##  [1789] 19999 20097 20195 20293 20391 20489 20587 20685 20783 20881 20979 21077
##  [1801] 21175 21273 21371 21469 21567 21665 21763 21861 21959 22057 22155 22253
##  [1813] 22351 22449 22547 22645 22743 22841 22939 23037 23135 23233 23331 23429
##  [1825] 23527 23625 23723 23821 23919 24017 24115 24213 24311 24409 24507 24605
##  [1837] 24703 24801 24899 24997 25095 25193 25291 25389 25487 25585 25683 25781
##  [1849]     8   106   204   302   400   498   596   694   792   890   988  1086
##  [1861]  1184  1282  1380  1478  1576  1674  1772  1870  1968  2066  2164  2262
##  [1873]  2360  2458  2556  2654  2752  2850  2948  3046  3144  3242  3340  3438
##  [1885]  3536  3634  3732  3830  3928  4026  4124  4222  4320  4418  4516  4614
##  [1897]  4712  4810  4908  5006  5104  5202  5300  5398  5496  5594  5692  5790
##  [1909]  5888  5986  6084  6182  6280  6378  6476  6574  6672  6770  6868  6966
##  [1921]  7064  7162  7260  7358  7456  7554  7652  7750  7848  7946  8044  8142
##  [1933]  8240  8338  8436  8534  8632  8730  8828  8926  9024  9122  9220  9318
##  [1945]  9416  9514  9612  9710  9808  9906 10004 10102 10200 10298 10396 10494
##  [1957] 10592 10690 10788 10886 10984 11082 11180 11278 11376 11474 11572 11670
##  [1969] 11768 11866 11964 12062 12160 12258 12356 12454 12552 12650 12748 12846
##  [1981] 12944 13042 13140 13238 13336 13434 13532 13630 13728 13826 13924 14022
##  [1993] 14120 14218 14316 14414 14512 14610 14708 14806 14904 15002 15100 15198
##  [2005] 15296 15394 15492 15590 15688 15786 15884 15982 16080 16178 16276 16374
##  [2017] 16472 16570 16668 16766 16864 16962 17060 17158 17256 17354 17452 17550
##  [2029] 17648 17746 17844 17942 18040 18138 18236 18334 18432 18530 18628 18726
##  [2041] 18824 18922 19020 19118 19216 19314 19412 19510 19608 19706 19804 19902
##  [2053] 20000 20098 20196 20294 20392 20490 20588 20686 20784 20882 20980 21078
##  [2065] 21176 21274 21372 21470 21568 21666 21764 21862 21960 22058 22156 22254
##  [2077] 22352 22450 22548 22646 22744 22842 22940 23038 23136 23234 23332 23430
##  [2089] 23528 23626 23724 23822 23920 24018 24116 24214 24312 24410 24508 24606
##  [2101] 24704 24802 24900 24998 25096 25194 25292 25390 25488 25586 25684 25782
##  [2113]     9   107   205   303   401   499   597   695   793   891   989  1087
##  [2125]  1185  1283  1381  1479  1577  1675  1773  1871  1969  2067  2165  2263
##  [2137]  2361  2459  2557  2655  2753  2851  2949  3047  3145  3243  3341  3439
##  [2149]  3537  3635  3733  3831  3929  4027  4125  4223  4321  4419  4517  4615
##  [2161]  4713  4811  4909  5007  5105  5203  5301  5399  5497  5595  5693  5791
##  [2173]  5889  5987  6085  6183  6281  6379  6477  6575  6673  6771  6869  6967
##  [2185]  7065  7163  7261  7359  7457  7555  7653  7751  7849  7947  8045  8143
##  [2197]  8241  8339  8437  8535  8633  8731  8829  8927  9025  9123  9221  9319
##  [2209]  9417  9515  9613  9711  9809  9907 10005 10103 10201 10299 10397 10495
##  [2221] 10593 10691 10789 10887 10985 11083 11181 11279 11377 11475 11573 11671
##  [2233] 11769 11867 11965 12063 12161 12259 12357 12455 12553 12651 12749 12847
##  [2245] 12945 13043 13141 13239 13337 13435 13533 13631 13729 13827 13925 14023
##  [2257] 14121 14219 14317 14415 14513 14611 14709 14807 14905 15003 15101 15199
##  [2269] 15297 15395 15493 15591 15689 15787 15885 15983 16081 16179 16277 16375
##  [2281] 16473 16571 16669 16767 16865 16963 17061 17159 17257 17355 17453 17551
##  [2293] 17649 17747 17845 17943 18041 18139 18237 18335 18433 18531 18629 18727
##  [2305] 18825 18923 19021 19119 19217 19315 19413 19511 19609 19707 19805 19903
##  [2317] 20001 20099 20197 20295 20393 20491 20589 20687 20785 20883 20981 21079
##  [2329] 21177 21275 21373 21471 21569 21667 21765 21863 21961 22059 22157 22255
##  [2341] 22353 22451 22549 22647 22745 22843 22941 23039 23137 23235 23333 23431
##  [2353] 23529 23627 23725 23823 23921 24019 24117 24215 24313 24411 24509 24607
##  [2365] 24705 24803 24901 24999 25097 25195 25293 25391 25489 25587 25685 25783
##  [2377]    10   108   206   304   402   500   598   696   794   892   990  1088
##  [2389]  1186  1284  1382  1480  1578  1676  1774  1872  1970  2068  2166  2264
##  [2401]  2362  2460  2558  2656  2754  2852  2950  3048  3146  3244  3342  3440
##  [2413]  3538  3636  3734  3832  3930  4028  4126  4224  4322  4420  4518  4616
##  [2425]  4714  4812  4910  5008  5106  5204  5302  5400  5498  5596  5694  5792
##  [2437]  5890  5988  6086  6184  6282  6380  6478  6576  6674  6772  6870  6968
##  [2449]  7066  7164  7262  7360  7458  7556  7654  7752  7850  7948  8046  8144
##  [2461]  8242  8340  8438  8536  8634  8732  8830  8928  9026  9124  9222  9320
##  [2473]  9418  9516  9614  9712  9810  9908 10006 10104 10202 10300 10398 10496
##  [2485] 10594 10692 10790 10888 10986 11084 11182 11280 11378 11476 11574 11672
##  [2497] 11770 11868 11966 12064 12162 12260 12358 12456 12554 12652 12750 12848
##  [2509] 12946 13044 13142 13240 13338 13436 13534 13632 13730 13828 13926 14024
##  [2521] 14122 14220 14318 14416 14514 14612 14710 14808 14906 15004 15102 15200
##  [2533] 15298 15396 15494 15592 15690 15788 15886 15984 16082 16180 16278 16376
##  [2545] 16474 16572 16670 16768 16866 16964 17062 17160 17258 17356 17454 17552
##  [2557] 17650 17748 17846 17944 18042 18140 18238 18336 18434 18532 18630 18728
##  [2569] 18826 18924 19022 19120 19218 19316 19414 19512 19610 19708 19806 19904
##  [2581] 20002 20100 20198 20296 20394 20492 20590 20688 20786 20884 20982 21080
##  [2593] 21178 21276 21374 21472 21570 21668 21766 21864 21962 22060 22158 22256
##  [2605] 22354 22452 22550 22648 22746 22844 22942 23040 23138 23236 23334 23432
##  [2617] 23530 23628 23726 23824 23922 24020 24118 24216 24314 24412 24510 24608
##  [2629] 24706 24804 24902 25000 25098 25196 25294 25392 25490 25588 25686 25784
##  [2641]    11   109   207   305   403   501   599   697   795   893   991  1089
##  [2653]  1187  1285  1383  1481  1579  1677  1775  1873  1971  2069  2167  2265
##  [2665]  2363  2461  2559  2657  2755  2853  2951  3049  3147  3245  3343  3441
##  [2677]  3539  3637  3735  3833  3931  4029  4127  4225  4323  4421  4519  4617
##  [2689]  4715  4813  4911  5009  5107  5205  5303  5401  5499  5597  5695  5793
##  [2701]  5891  5989  6087  6185  6283  6381  6479  6577  6675  6773  6871  6969
##  [2713]  7067  7165  7263  7361  7459  7557  7655  7753  7851  7949  8047  8145
##  [2725]  8243  8341  8439  8537  8635  8733  8831  8929  9027  9125  9223  9321
##  [2737]  9419  9517  9615  9713  9811  9909 10007 10105 10203 10301 10399 10497
##  [2749] 10595 10693 10791 10889 10987 11085 11183 11281 11379 11477 11575 11673
##  [2761] 11771 11869 11967 12065 12163 12261 12359 12457 12555 12653 12751 12849
##  [2773] 12947 13045 13143 13241 13339 13437 13535 13633 13731 13829 13927 14025
##  [2785] 14123 14221 14319 14417 14515 14613 14711 14809 14907 15005 15103 15201
##  [2797] 15299 15397 15495 15593 15691 15789 15887 15985 16083 16181 16279 16377
##  [2809] 16475 16573 16671 16769 16867 16965 17063 17161 17259 17357 17455 17553
##  [2821] 17651 17749 17847 17945 18043 18141 18239 18337 18435 18533 18631 18729
##  [2833] 18827 18925 19023 19121 19219 19317 19415 19513 19611 19709 19807 19905
##  [2845] 20003 20101 20199 20297 20395 20493 20591 20689 20787 20885 20983 21081
##  [2857] 21179 21277 21375 21473 21571 21669 21767 21865 21963 22061 22159 22257
##  [2869] 22355 22453 22551 22649 22747 22845 22943 23041 23139 23237 23335 23433
##  [2881] 23531 23629 23727 23825 23923 24021 24119 24217 24315 24413 24511 24609
##  [2893] 24707 24805 24903 25001 25099 25197 25295 25393 25491 25589 25687 25785
##  [2905]    12   110   208   306   404   502   600   698   796   894   992  1090
##  [2917]  1188  1286  1384  1482  1580  1678  1776  1874  1972  2070  2168  2266
##  [2929]  2364  2462  2560  2658  2756  2854  2952  3050  3148  3246  3344  3442
##  [2941]  3540  3638  3736  3834  3932  4030  4128  4226  4324  4422  4520  4618
##  [2953]  4716  4814  4912  5010  5108  5206  5304  5402  5500  5598  5696  5794
##  [2965]  5892  5990  6088  6186  6284  6382  6480  6578  6676  6774  6872  6970
##  [2977]  7068  7166  7264  7362  7460  7558  7656  7754  7852  7950  8048  8146
##  [2989]  8244  8342  8440  8538  8636  8734  8832  8930  9028  9126  9224  9322
##  [3001]  9420  9518  9616  9714  9812  9910 10008 10106 10204 10302 10400 10498
##  [3013] 10596 10694 10792 10890 10988 11086 11184 11282 11380 11478 11576 11674
##  [3025] 11772 11870 11968 12066 12164 12262 12360 12458 12556 12654 12752 12850
##  [3037] 12948 13046 13144 13242 13340 13438 13536 13634 13732 13830 13928 14026
##  [3049] 14124 14222 14320 14418 14516 14614 14712 14810 14908 15006 15104 15202
##  [3061] 15300 15398 15496 15594 15692 15790 15888 15986 16084 16182 16280 16378
##  [3073] 16476 16574 16672 16770 16868 16966 17064 17162 17260 17358 17456 17554
##  [3085] 17652 17750 17848 17946 18044 18142 18240 18338 18436 18534 18632 18730
##  [3097] 18828 18926 19024 19122 19220 19318 19416 19514 19612 19710 19808 19906
##  [3109] 20004 20102 20200 20298 20396 20494 20592 20690 20788 20886 20984 21082
##  [3121] 21180 21278 21376 21474 21572 21670 21768 21866 21964 22062 22160 22258
##  [3133] 22356 22454 22552 22650 22748 22846 22944 23042 23140 23238 23336 23434
##  [3145] 23532 23630 23728 23826 23924 24022 24120 24218 24316 24414 24512 24610
##  [3157] 24708 24806 24904 25002 25100 25198 25296 25394 25492 25590 25688 25786
##  [3169]    13   111   209   307   405   503   601   699   797   895   993  1091
##  [3181]  1189  1287  1385  1483  1581  1679  1777  1875  1973  2071  2169  2267
##  [3193]  2365  2463  2561  2659  2757  2855  2953  3051  3149  3247  3345  3443
##  [3205]  3541  3639  3737  3835  3933  4031  4129  4227  4325  4423  4521  4619
##  [3217]  4717  4815  4913  5011  5109  5207  5305  5403  5501  5599  5697  5795
##  [3229]  5893  5991  6089  6187  6285  6383  6481  6579  6677  6775  6873  6971
##  [3241]  7069  7167  7265  7363  7461  7559  7657  7755  7853  7951  8049  8147
##  [3253]  8245  8343  8441  8539  8637  8735  8833  8931  9029  9127  9225  9323
##  [3265]  9421  9519  9617  9715  9813  9911 10009 10107 10205 10303 10401 10499
##  [3277] 10597 10695 10793 10891 10989 11087 11185 11283 11381 11479 11577 11675
##  [3289] 11773 11871 11969 12067 12165 12263 12361 12459 12557 12655 12753 12851
##  [3301] 12949 13047 13145 13243 13341 13439 13537 13635 13733 13831 13929 14027
##  [3313] 14125 14223 14321 14419 14517 14615 14713 14811 14909 15007 15105 15203
##  [3325] 15301 15399 15497 15595 15693 15791 15889 15987 16085 16183 16281 16379
##  [3337] 16477 16575 16673 16771 16869 16967 17065 17163 17261 17359 17457 17555
##  [3349] 17653 17751 17849 17947 18045 18143 18241 18339 18437 18535 18633 18731
##  [3361] 18829 18927 19025 19123 19221 19319 19417 19515 19613 19711 19809 19907
##  [3373] 20005 20103 20201 20299 20397 20495 20593 20691 20789 20887 20985 21083
##  [3385] 21181 21279 21377 21475 21573 21671 21769 21867 21965 22063 22161 22259
##  [3397] 22357 22455 22553 22651 22749 22847 22945 23043 23141 23239 23337 23435
##  [3409] 23533 23631 23729 23827 23925 24023 24121 24219 24317 24415 24513 24611
##  [3421] 24709 24807 24905 25003 25101 25199 25297 25395 25493 25591 25689 25787
##  [3433]    14   112   210   308   406   504   602   700   798   896   994  1092
##  [3445]  1190  1288  1386  1484  1582  1680  1778  1876  1974  2072  2170  2268
##  [3457]  2366  2464  2562  2660  2758  2856  2954  3052  3150  3248  3346  3444
##  [3469]  3542  3640  3738  3836  3934  4032  4130  4228  4326  4424  4522  4620
##  [3481]  4718  4816  4914  5012  5110  5208  5306  5404  5502  5600  5698  5796
##  [3493]  5894  5992  6090  6188  6286  6384  6482  6580  6678  6776  6874  6972
##  [3505]  7070  7168  7266  7364  7462  7560  7658  7756  7854  7952  8050  8148
##  [3517]  8246  8344  8442  8540  8638  8736  8834  8932  9030  9128  9226  9324
##  [3529]  9422  9520  9618  9716  9814  9912 10010 10108 10206 10304 10402 10500
##  [3541] 10598 10696 10794 10892 10990 11088 11186 11284 11382 11480 11578 11676
##  [3553] 11774 11872 11970 12068 12166 12264 12362 12460 12558 12656 12754 12852
##  [3565] 12950 13048 13146 13244 13342 13440 13538 13636 13734 13832 13930 14028
##  [3577] 14126 14224 14322 14420 14518 14616 14714 14812 14910 15008 15106 15204
##  [3589] 15302 15400 15498 15596 15694 15792 15890 15988 16086 16184 16282 16380
##  [3601] 16478 16576 16674 16772 16870 16968 17066 17164 17262 17360 17458 17556
##  [3613] 17654 17752 17850 17948 18046 18144 18242 18340 18438 18536 18634 18732
##  [3625] 18830 18928 19026 19124 19222 19320 19418 19516 19614 19712 19810 19908
##  [3637] 20006 20104 20202 20300 20398 20496 20594 20692 20790 20888 20986 21084
##  [3649] 21182 21280 21378 21476 21574 21672 21770 21868 21966 22064 22162 22260
##  [3661] 22358 22456 22554 22652 22750 22848 22946 23044 23142 23240 23338 23436
##  [3673] 23534 23632 23730 23828 23926 24024 24122 24220 24318 24416 24514 24612
##  [3685] 24710 24808 24906 25004 25102 25200 25298 25396 25494 25592 25690 25788
##  [3697]    15   113   211   309   407   505   603   701   799   897   995  1093
##  [3709]  1191  1289  1387  1485  1583  1681  1779  1877  1975  2073  2171  2269
##  [3721]  2367  2465  2563  2661  2759  2857  2955  3053  3151  3249  3347  3445
##  [3733]  3543  3641  3739  3837  3935  4033  4131  4229  4327  4425  4523  4621
##  [3745]  4719  4817  4915  5013  5111  5209  5307  5405  5503  5601  5699  5797
##  [3757]  5895  5993  6091  6189  6287  6385  6483  6581  6679  6777  6875  6973
##  [3769]  7071  7169  7267  7365  7463  7561  7659  7757  7855  7953  8051  8149
##  [3781]  8247  8345  8443  8541  8639  8737  8835  8933  9031  9129  9227  9325
##  [3793]  9423  9521  9619  9717  9815  9913 10011 10109 10207 10305 10403 10501
##  [3805] 10599 10697 10795 10893 10991 11089 11187 11285 11383 11481 11579 11677
##  [3817] 11775 11873 11971 12069 12167 12265 12363 12461 12559 12657 12755 12853
##  [3829] 12951 13049 13147 13245 13343 13441 13539 13637 13735 13833 13931 14029
##  [3841] 14127 14225 14323 14421 14519 14617 14715 14813 14911 15009 15107 15205
##  [3853] 15303 15401 15499 15597 15695 15793 15891 15989 16087 16185 16283 16381
##  [3865] 16479 16577 16675 16773 16871 16969 17067 17165 17263 17361 17459 17557
##  [3877] 17655 17753 17851 17949 18047 18145 18243 18341 18439 18537 18635 18733
##  [3889] 18831 18929 19027 19125 19223 19321 19419 19517 19615 19713 19811 19909
##  [3901] 20007 20105 20203 20301 20399 20497 20595 20693 20791 20889 20987 21085
##  [3913] 21183 21281 21379 21477 21575 21673 21771 21869 21967 22065 22163 22261
##  [3925] 22359 22457 22555 22653 22751 22849 22947 23045 23143 23241 23339 23437
##  [3937] 23535 23633 23731 23829 23927 24025 24123 24221 24319 24417 24515 24613
##  [3949] 24711 24809 24907 25005 25103 25201 25299 25397 25495 25593 25691 25789
##  [3961]    16   114   212   310   408   506   604   702   800   898   996  1094
##  [3973]  1192  1290  1388  1486  1584  1682  1780  1878  1976  2074  2172  2270
##  [3985]  2368  2466  2564  2662  2760  2858  2956  3054  3152  3250  3348  3446
##  [3997]  3544  3642  3740  3838  3936  4034  4132  4230  4328  4426  4524  4622
##  [4009]  4720  4818  4916  5014  5112  5210  5308  5406  5504  5602  5700  5798
##  [4021]  5896  5994  6092  6190  6288  6386  6484  6582  6680  6778  6876  6974
##  [4033]  7072  7170  7268  7366  7464  7562  7660  7758  7856  7954  8052  8150
##  [4045]  8248  8346  8444  8542  8640  8738  8836  8934  9032  9130  9228  9326
##  [4057]  9424  9522  9620  9718  9816  9914 10012 10110 10208 10306 10404 10502
##  [4069] 10600 10698 10796 10894 10992 11090 11188 11286 11384 11482 11580 11678
##  [4081] 11776 11874 11972 12070 12168 12266 12364 12462 12560 12658 12756 12854
##  [4093] 12952 13050 13148 13246 13344 13442 13540 13638 13736 13834 13932 14030
##  [4105] 14128 14226 14324 14422 14520 14618 14716 14814 14912 15010 15108 15206
##  [4117] 15304 15402 15500 15598 15696 15794 15892 15990 16088 16186 16284 16382
##  [4129] 16480 16578 16676 16774 16872 16970 17068 17166 17264 17362 17460 17558
##  [4141] 17656 17754 17852 17950 18048 18146 18244 18342 18440 18538 18636 18734
##  [4153] 18832 18930 19028 19126 19224 19322 19420 19518 19616 19714 19812 19910
##  [4165] 20008 20106 20204 20302 20400 20498 20596 20694 20792 20890 20988 21086
##  [4177] 21184 21282 21380 21478 21576 21674 21772 21870 21968 22066 22164 22262
##  [4189] 22360 22458 22556 22654 22752 22850 22948 23046 23144 23242 23340 23438
##  [4201] 23536 23634 23732 23830 23928 24026 24124 24222 24320 24418 24516 24614
##  [4213] 24712 24810 24908 25006 25104 25202 25300 25398 25496 25594 25692 25790
##  [4225]    17   115   213   311   409   507   605   703   801   899   997  1095
##  [4237]  1193  1291  1389  1487  1585  1683  1781  1879  1977  2075  2173  2271
##  [4249]  2369  2467  2565  2663  2761  2859  2957  3055  3153  3251  3349  3447
##  [4261]  3545  3643  3741  3839  3937  4035  4133  4231  4329  4427  4525  4623
##  [4273]  4721  4819  4917  5015  5113  5211  5309  5407  5505  5603  5701  5799
##  [4285]  5897  5995  6093  6191  6289  6387  6485  6583  6681  6779  6877  6975
##  [4297]  7073  7171  7269  7367  7465  7563  7661  7759  7857  7955  8053  8151
##  [4309]  8249  8347  8445  8543  8641  8739  8837  8935  9033  9131  9229  9327
##  [4321]  9425  9523  9621  9719  9817  9915 10013 10111 10209 10307 10405 10503
##  [4333] 10601 10699 10797 10895 10993 11091 11189 11287 11385 11483 11581 11679
##  [4345] 11777 11875 11973 12071 12169 12267 12365 12463 12561 12659 12757 12855
##  [4357] 12953 13051 13149 13247 13345 13443 13541 13639 13737 13835 13933 14031
##  [4369] 14129 14227 14325 14423 14521 14619 14717 14815 14913 15011 15109 15207
##  [4381] 15305 15403 15501 15599 15697 15795 15893 15991 16089 16187 16285 16383
##  [4393] 16481 16579 16677 16775 16873 16971 17069 17167 17265 17363 17461 17559
##  [4405] 17657 17755 17853 17951 18049 18147 18245 18343 18441 18539 18637 18735
##  [4417] 18833 18931 19029 19127 19225 19323 19421 19519 19617 19715 19813 19911
##  [4429] 20009 20107 20205 20303 20401 20499 20597 20695 20793 20891 20989 21087
##  [4441] 21185 21283 21381 21479 21577 21675 21773 21871 21969 22067 22165 22263
##  [4453] 22361 22459 22557 22655 22753 22851 22949 23047 23145 23243 23341 23439
##  [4465] 23537 23635 23733 23831 23929 24027 24125 24223 24321 24419 24517 24615
##  [4477] 24713 24811 24909 25007 25105 25203 25301 25399 25497 25595 25693 25791
##  [4489]    18   116   214   312   410   508   606   704   802   900   998  1096
##  [4501]  1194  1292  1390  1488  1586  1684  1782  1880  1978  2076  2174  2272
##  [4513]  2370  2468  2566  2664  2762  2860  2958  3056  3154  3252  3350  3448
##  [4525]  3546  3644  3742  3840  3938  4036  4134  4232  4330  4428  4526  4624
##  [4537]  4722  4820  4918  5016  5114  5212  5310  5408  5506  5604  5702  5800
##  [4549]  5898  5996  6094  6192  6290  6388  6486  6584  6682  6780  6878  6976
##  [4561]  7074  7172  7270  7368  7466  7564  7662  7760  7858  7956  8054  8152
##  [4573]  8250  8348  8446  8544  8642  8740  8838  8936  9034  9132  9230  9328
##  [4585]  9426  9524  9622  9720  9818  9916 10014 10112 10210 10308 10406 10504
##  [4597] 10602 10700 10798 10896 10994 11092 11190 11288 11386 11484 11582 11680
##  [4609] 11778 11876 11974 12072 12170 12268 12366 12464 12562 12660 12758 12856
##  [4621] 12954 13052 13150 13248 13346 13444 13542 13640 13738 13836 13934 14032
##  [4633] 14130 14228 14326 14424 14522 14620 14718 14816 14914 15012 15110 15208
##  [4645] 15306 15404 15502 15600 15698 15796 15894 15992 16090 16188 16286 16384
##  [4657] 16482 16580 16678 16776 16874 16972 17070 17168 17266 17364 17462 17560
##  [4669] 17658 17756 17854 17952 18050 18148 18246 18344 18442 18540 18638 18736
##  [4681] 18834 18932 19030 19128 19226 19324 19422 19520 19618 19716 19814 19912
##  [4693] 20010 20108 20206 20304 20402 20500 20598 20696 20794 20892 20990 21088
##  [4705] 21186 21284 21382 21480 21578 21676 21774 21872 21970 22068 22166 22264
##  [4717] 22362 22460 22558 22656 22754 22852 22950 23048 23146 23244 23342 23440
##  [4729] 23538 23636 23734 23832 23930 24028 24126 24224 24322 24420 24518 24616
##  [4741] 24714 24812 24910 25008 25106 25204 25302 25400 25498 25596 25694 25792
##  [4753]    19   117   215   313   411   509   607   705   803   901   999  1097
##  [4765]  1195  1293  1391  1489  1587  1685  1783  1881  1979  2077  2175  2273
##  [4777]  2371  2469  2567  2665  2763  2861  2959  3057  3155  3253  3351  3449
##  [4789]  3547  3645  3743  3841  3939  4037  4135  4233  4331  4429  4527  4625
##  [4801]  4723  4821  4919  5017  5115  5213  5311  5409  5507  5605  5703  5801
##  [4813]  5899  5997  6095  6193  6291  6389  6487  6585  6683  6781  6879  6977
##  [4825]  7075  7173  7271  7369  7467  7565  7663  7761  7859  7957  8055  8153
##  [4837]  8251  8349  8447  8545  8643  8741  8839  8937  9035  9133  9231  9329
##  [4849]  9427  9525  9623  9721  9819  9917 10015 10113 10211 10309 10407 10505
##  [4861] 10603 10701 10799 10897 10995 11093 11191 11289 11387 11485 11583 11681
##  [4873] 11779 11877 11975 12073 12171 12269 12367 12465 12563 12661 12759 12857
##  [4885] 12955 13053 13151 13249 13347 13445 13543 13641 13739 13837 13935 14033
##  [4897] 14131 14229 14327 14425 14523 14621 14719 14817 14915 15013 15111 15209
##  [4909] 15307 15405 15503 15601 15699 15797 15895 15993 16091 16189 16287 16385
##  [4921] 16483 16581 16679 16777 16875 16973 17071 17169 17267 17365 17463 17561
##  [4933] 17659 17757 17855 17953 18051 18149 18247 18345 18443 18541 18639 18737
##  [4945] 18835 18933 19031 19129 19227 19325 19423 19521 19619 19717 19815 19913
##  [4957] 20011 20109 20207 20305 20403 20501 20599 20697 20795 20893 20991 21089
##  [4969] 21187 21285 21383 21481 21579 21677 21775 21873 21971 22069 22167 22265
##  [4981] 22363 22461 22559 22657 22755 22853 22951 23049 23147 23245 23343 23441
##  [4993] 23539 23637 23735 23833 23931 24029 24127 24225 24323 24421 24519 24617
##  [5005] 24715 24813 24911 25009 25107 25205 25303 25401 25499 25597 25695 25793
##  [5017]    20   118   216   314   412   510   608   706   804   902  1000  1098
##  [5029]  1196  1294  1392  1490  1588  1686  1784  1882  1980  2078  2176  2274
##  [5041]  2372  2470  2568  2666  2764  2862  2960  3058  3156  3254  3352  3450
##  [5053]  3548  3646  3744  3842  3940  4038  4136  4234  4332  4430  4528  4626
##  [5065]  4724  4822  4920  5018  5116  5214  5312  5410  5508  5606  5704  5802
##  [5077]  5900  5998  6096  6194  6292  6390  6488  6586  6684  6782  6880  6978
##  [5089]  7076  7174  7272  7370  7468  7566  7664  7762  7860  7958  8056  8154
##  [5101]  8252  8350  8448  8546  8644  8742  8840  8938  9036  9134  9232  9330
##  [5113]  9428  9526  9624  9722  9820  9918 10016 10114 10212 10310 10408 10506
##  [5125] 10604 10702 10800 10898 10996 11094 11192 11290 11388 11486 11584 11682
##  [5137] 11780 11878 11976 12074 12172 12270 12368 12466 12564 12662 12760 12858
##  [5149] 12956 13054 13152 13250 13348 13446 13544 13642 13740 13838 13936 14034
##  [5161] 14132 14230 14328 14426 14524 14622 14720 14818 14916 15014 15112 15210
##  [5173] 15308 15406 15504 15602 15700 15798 15896 15994 16092 16190 16288 16386
##  [5185] 16484 16582 16680 16778 16876 16974 17072 17170 17268 17366 17464 17562
##  [5197] 17660 17758 17856 17954 18052 18150 18248 18346 18444 18542 18640 18738
##  [5209] 18836 18934 19032 19130 19228 19326 19424 19522 19620 19718 19816 19914
##  [5221] 20012 20110 20208 20306 20404 20502 20600 20698 20796 20894 20992 21090
##  [5233] 21188 21286 21384 21482 21580 21678 21776 21874 21972 22070 22168 22266
##  [5245] 22364 22462 22560 22658 22756 22854 22952 23050 23148 23246 23344 23442
##  [5257] 23540 23638 23736 23834 23932 24030 24128 24226 24324 24422 24520 24618
##  [5269] 24716 24814 24912 25010 25108 25206 25304 25402 25500 25598 25696 25794
##  [5281]    21   119   217   315   413   511   609   707   805   903  1001  1099
##  [5293]  1197  1295  1393  1491  1589  1687  1785  1883  1981  2079  2177  2275
##  [5305]  2373  2471  2569  2667  2765  2863  2961  3059  3157  3255  3353  3451
##  [5317]  3549  3647  3745  3843  3941  4039  4137  4235  4333  4431  4529  4627
##  [5329]  4725  4823  4921  5019  5117  5215  5313  5411  5509  5607  5705  5803
##  [5341]  5901  5999  6097  6195  6293  6391  6489  6587  6685  6783  6881  6979
##  [5353]  7077  7175  7273  7371  7469  7567  7665  7763  7861  7959  8057  8155
##  [5365]  8253  8351  8449  8547  8645  8743  8841  8939  9037  9135  9233  9331
##  [5377]  9429  9527  9625  9723  9821  9919 10017 10115 10213 10311 10409 10507
##  [5389] 10605 10703 10801 10899 10997 11095 11193 11291 11389 11487 11585 11683
##  [5401] 11781 11879 11977 12075 12173 12271 12369 12467 12565 12663 12761 12859
##  [5413] 12957 13055 13153 13251 13349 13447 13545 13643 13741 13839 13937 14035
##  [5425] 14133 14231 14329 14427 14525 14623 14721 14819 14917 15015 15113 15211
##  [5437] 15309 15407 15505 15603 15701 15799 15897 15995 16093 16191 16289 16387
##  [5449] 16485 16583 16681 16779 16877 16975 17073 17171 17269 17367 17465 17563
##  [5461] 17661 17759 17857 17955 18053 18151 18249 18347 18445 18543 18641 18739
##  [5473] 18837 18935 19033 19131 19229 19327 19425 19523 19621 19719 19817 19915
##  [5485] 20013 20111 20209 20307 20405 20503 20601 20699 20797 20895 20993 21091
##  [5497] 21189 21287 21385 21483 21581 21679 21777 21875 21973 22071 22169 22267
##  [5509] 22365 22463 22561 22659 22757 22855 22953 23051 23149 23247 23345 23443
##  [5521] 23541 23639 23737 23835 23933 24031 24129 24227 24325 24423 24521 24619
##  [5533] 24717 24815 24913 25011 25109 25207 25305 25403 25501 25599 25697 25795
##  [5545]    22   120   218   316   414   512   610   708   806   904  1002  1100
##  [5557]  1198  1296  1394  1492  1590  1688  1786  1884  1982  2080  2178  2276
##  [5569]  2374  2472  2570  2668  2766  2864  2962  3060  3158  3256  3354  3452
##  [5581]  3550  3648  3746  3844  3942  4040  4138  4236  4334  4432  4530  4628
##  [5593]  4726  4824  4922  5020  5118  5216  5314  5412  5510  5608  5706  5804
##  [5605]  5902  6000  6098  6196  6294  6392  6490  6588  6686  6784  6882  6980
##  [5617]  7078  7176  7274  7372  7470  7568  7666  7764  7862  7960  8058  8156
##  [5629]  8254  8352  8450  8548  8646  8744  8842  8940  9038  9136  9234  9332
##  [5641]  9430  9528  9626  9724  9822  9920 10018 10116 10214 10312 10410 10508
##  [5653] 10606 10704 10802 10900 10998 11096 11194 11292 11390 11488 11586 11684
##  [5665] 11782 11880 11978 12076 12174 12272 12370 12468 12566 12664 12762 12860
##  [5677] 12958 13056 13154 13252 13350 13448 13546 13644 13742 13840 13938 14036
##  [5689] 14134 14232 14330 14428 14526 14624 14722 14820 14918 15016 15114 15212
##  [5701] 15310 15408 15506 15604 15702 15800 15898 15996 16094 16192 16290 16388
##  [5713] 16486 16584 16682 16780 16878 16976 17074 17172 17270 17368 17466 17564
##  [5725] 17662 17760 17858 17956 18054 18152 18250 18348 18446 18544 18642 18740
##  [5737] 18838 18936 19034 19132 19230 19328 19426 19524 19622 19720 19818 19916
##  [5749] 20014 20112 20210 20308 20406 20504 20602 20700 20798 20896 20994 21092
##  [5761] 21190 21288 21386 21484 21582 21680 21778 21876 21974 22072 22170 22268
##  [5773] 22366 22464 22562 22660 22758 22856 22954 23052 23150 23248 23346 23444
##  [5785] 23542 23640 23738 23836 23934 24032 24130 24228 24326 24424 24522 24620
##  [5797] 24718 24816 24914 25012 25110 25208 25306 25404 25502 25600 25698 25796
##  [5809]    23   121   219   317   415   513   611   709   807   905  1003  1101
##  [5821]  1199  1297  1395  1493  1591  1689  1787  1885  1983  2081  2179  2277
##  [5833]  2375  2473  2571  2669  2767  2865  2963  3061  3159  3257  3355  3453
##  [5845]  3551  3649  3747  3845  3943  4041  4139  4237  4335  4433  4531  4629
##  [5857]  4727  4825  4923  5021  5119  5217  5315  5413  5511  5609  5707  5805
##  [5869]  5903  6001  6099  6197  6295  6393  6491  6589  6687  6785  6883  6981
##  [5881]  7079  7177  7275  7373  7471  7569  7667  7765  7863  7961  8059  8157
##  [5893]  8255  8353  8451  8549  8647  8745  8843  8941  9039  9137  9235  9333
##  [5905]  9431  9529  9627  9725  9823  9921 10019 10117 10215 10313 10411 10509
##  [5917] 10607 10705 10803 10901 10999 11097 11195 11293 11391 11489 11587 11685
##  [5929] 11783 11881 11979 12077 12175 12273 12371 12469 12567 12665 12763 12861
##  [5941] 12959 13057 13155 13253 13351 13449 13547 13645 13743 13841 13939 14037
##  [5953] 14135 14233 14331 14429 14527 14625 14723 14821 14919 15017 15115 15213
##  [5965] 15311 15409 15507 15605 15703 15801 15899 15997 16095 16193 16291 16389
##  [5977] 16487 16585 16683 16781 16879 16977 17075 17173 17271 17369 17467 17565
##  [5989] 17663 17761 17859 17957 18055 18153 18251 18349 18447 18545 18643 18741
##  [6001] 18839 18937 19035 19133 19231 19329 19427 19525 19623 19721 19819 19917
##  [6013] 20015 20113 20211 20309 20407 20505 20603 20701 20799 20897 20995 21093
##  [6025] 21191 21289 21387 21485 21583 21681 21779 21877 21975 22073 22171 22269
##  [6037] 22367 22465 22563 22661 22759 22857 22955 23053 23151 23249 23347 23445
##  [6049] 23543 23641 23739 23837 23935 24033 24131 24229 24327 24425 24523 24621
##  [6061] 24719 24817 24915 25013 25111 25209 25307 25405 25503 25601 25699 25797
##  [6073]    24   122   220   318   416   514   612   710   808   906  1004  1102
##  [6085]  1200  1298  1396  1494  1592  1690  1788  1886  1984  2082  2180  2278
##  [6097]  2376  2474  2572  2670  2768  2866  2964  3062  3160  3258  3356  3454
##  [6109]  3552  3650  3748  3846  3944  4042  4140  4238  4336  4434  4532  4630
##  [6121]  4728  4826  4924  5022  5120  5218  5316  5414  5512  5610  5708  5806
##  [6133]  5904  6002  6100  6198  6296  6394  6492  6590  6688  6786  6884  6982
##  [6145]  7080  7178  7276  7374  7472  7570  7668  7766  7864  7962  8060  8158
##  [6157]  8256  8354  8452  8550  8648  8746  8844  8942  9040  9138  9236  9334
##  [6169]  9432  9530  9628  9726  9824  9922 10020 10118 10216 10314 10412 10510
##  [6181] 10608 10706 10804 10902 11000 11098 11196 11294 11392 11490 11588 11686
##  [6193] 11784 11882 11980 12078 12176 12274 12372 12470 12568 12666 12764 12862
##  [6205] 12960 13058 13156 13254 13352 13450 13548 13646 13744 13842 13940 14038
##  [6217] 14136 14234 14332 14430 14528 14626 14724 14822 14920 15018 15116 15214
##  [6229] 15312 15410 15508 15606 15704 15802 15900 15998 16096 16194 16292 16390
##  [6241] 16488 16586 16684 16782 16880 16978 17076 17174 17272 17370 17468 17566
##  [6253] 17664 17762 17860 17958 18056 18154 18252 18350 18448 18546 18644 18742
##  [6265] 18840 18938 19036 19134 19232 19330 19428 19526 19624 19722 19820 19918
##  [6277] 20016 20114 20212 20310 20408 20506 20604 20702 20800 20898 20996 21094
##  [6289] 21192 21290 21388 21486 21584 21682 21780 21878 21976 22074 22172 22270
##  [6301] 22368 22466 22564 22662 22760 22858 22956 23054 23152 23250 23348 23446
##  [6313] 23544 23642 23740 23838 23936 24034 24132 24230 24328 24426 24524 24622
##  [6325] 24720 24818 24916 25014 25112 25210 25308 25406 25504 25602 25700 25798
##  [6337]    25   123   221   319   417   515   613   711   809   907  1005  1103
##  [6349]  1201  1299  1397  1495  1593  1691  1789  1887  1985  2083  2181  2279
##  [6361]  2377  2475  2573  2671  2769  2867  2965  3063  3161  3259  3357  3455
##  [6373]  3553  3651  3749  3847  3945  4043  4141  4239  4337  4435  4533  4631
##  [6385]  4729  4827  4925  5023  5121  5219  5317  5415  5513  5611  5709  5807
##  [6397]  5905  6003  6101  6199  6297  6395  6493  6591  6689  6787  6885  6983
##  [6409]  7081  7179  7277  7375  7473  7571  7669  7767  7865  7963  8061  8159
##  [6421]  8257  8355  8453  8551  8649  8747  8845  8943  9041  9139  9237  9335
##  [6433]  9433  9531  9629  9727  9825  9923 10021 10119 10217 10315 10413 10511
##  [6445] 10609 10707 10805 10903 11001 11099 11197 11295 11393 11491 11589 11687
##  [6457] 11785 11883 11981 12079 12177 12275 12373 12471 12569 12667 12765 12863
##  [6469] 12961 13059 13157 13255 13353 13451 13549 13647 13745 13843 13941 14039
##  [6481] 14137 14235 14333 14431 14529 14627 14725 14823 14921 15019 15117 15215
##  [6493] 15313 15411 15509 15607 15705 15803 15901 15999 16097 16195 16293 16391
##  [6505] 16489 16587 16685 16783 16881 16979 17077 17175 17273 17371 17469 17567
##  [6517] 17665 17763 17861 17959 18057 18155 18253 18351 18449 18547 18645 18743
##  [6529] 18841 18939 19037 19135 19233 19331 19429 19527 19625 19723 19821 19919
##  [6541] 20017 20115 20213 20311 20409 20507 20605 20703 20801 20899 20997 21095
##  [6553] 21193 21291 21389 21487 21585 21683 21781 21879 21977 22075 22173 22271
##  [6565] 22369 22467 22565 22663 22761 22859 22957 23055 23153 23251 23349 23447
##  [6577] 23545 23643 23741 23839 23937 24035 24133 24231 24329 24427 24525 24623
##  [6589] 24721 24819 24917 25015 25113 25211 25309 25407 25505 25603 25701 25799
##  [6601]    26   124   222   320   418   516   614   712   810   908  1006  1104
##  [6613]  1202  1300  1398  1496  1594  1692  1790  1888  1986  2084  2182  2280
##  [6625]  2378  2476  2574  2672  2770  2868  2966  3064  3162  3260  3358  3456
##  [6637]  3554  3652  3750  3848  3946  4044  4142  4240  4338  4436  4534  4632
##  [6649]  4730  4828  4926  5024  5122  5220  5318  5416  5514  5612  5710  5808
##  [6661]  5906  6004  6102  6200  6298  6396  6494  6592  6690  6788  6886  6984
##  [6673]  7082  7180  7278  7376  7474  7572  7670  7768  7866  7964  8062  8160
##  [6685]  8258  8356  8454  8552  8650  8748  8846  8944  9042  9140  9238  9336
##  [6697]  9434  9532  9630  9728  9826  9924 10022 10120 10218 10316 10414 10512
##  [6709] 10610 10708 10806 10904 11002 11100 11198 11296 11394 11492 11590 11688
##  [6721] 11786 11884 11982 12080 12178 12276 12374 12472 12570 12668 12766 12864
##  [6733] 12962 13060 13158 13256 13354 13452 13550 13648 13746 13844 13942 14040
##  [6745] 14138 14236 14334 14432 14530 14628 14726 14824 14922 15020 15118 15216
##  [6757] 15314 15412 15510 15608 15706 15804 15902 16000 16098 16196 16294 16392
##  [6769] 16490 16588 16686 16784 16882 16980 17078 17176 17274 17372 17470 17568
##  [6781] 17666 17764 17862 17960 18058 18156 18254 18352 18450 18548 18646 18744
##  [6793] 18842 18940 19038 19136 19234 19332 19430 19528 19626 19724 19822 19920
##  [6805] 20018 20116 20214 20312 20410 20508 20606 20704 20802 20900 20998 21096
##  [6817] 21194 21292 21390 21488 21586 21684 21782 21880 21978 22076 22174 22272
##  [6829] 22370 22468 22566 22664 22762 22860 22958 23056 23154 23252 23350 23448
##  [6841] 23546 23644 23742 23840 23938 24036 24134 24232 24330 24428 24526 24624
##  [6853] 24722 24820 24918 25016 25114 25212 25310 25408 25506 25604 25702 25800
##  [6865]    27   125   223   321   419   517   615   713   811   909  1007  1105
##  [6877]  1203  1301  1399  1497  1595  1693  1791  1889  1987  2085  2183  2281
##  [6889]  2379  2477  2575  2673  2771  2869  2967  3065  3163  3261  3359  3457
##  [6901]  3555  3653  3751  3849  3947  4045  4143  4241  4339  4437  4535  4633
##  [6913]  4731  4829  4927  5025  5123  5221  5319  5417  5515  5613  5711  5809
##  [6925]  5907  6005  6103  6201  6299  6397  6495  6593  6691  6789  6887  6985
##  [6937]  7083  7181  7279  7377  7475  7573  7671  7769  7867  7965  8063  8161
##  [6949]  8259  8357  8455  8553  8651  8749  8847  8945  9043  9141  9239  9337
##  [6961]  9435  9533  9631  9729  9827  9925 10023 10121 10219 10317 10415 10513
##  [6973] 10611 10709 10807 10905 11003 11101 11199 11297 11395 11493 11591 11689
##  [6985] 11787 11885 11983 12081 12179 12277 12375 12473 12571 12669 12767 12865
##  [6997] 12963 13061 13159 13257 13355 13453 13551 13649 13747 13845 13943 14041
##  [7009] 14139 14237 14335 14433 14531 14629 14727 14825 14923 15021 15119 15217
##  [7021] 15315 15413 15511 15609 15707 15805 15903 16001 16099 16197 16295 16393
##  [7033] 16491 16589 16687 16785 16883 16981 17079 17177 17275 17373 17471 17569
##  [7045] 17667 17765 17863 17961 18059 18157 18255 18353 18451 18549 18647 18745
##  [7057] 18843 18941 19039 19137 19235 19333 19431 19529 19627 19725 19823 19921
##  [7069] 20019 20117 20215 20313 20411 20509 20607 20705 20803 20901 20999 21097
##  [7081] 21195 21293 21391 21489 21587 21685 21783 21881 21979 22077 22175 22273
##  [7093] 22371 22469 22567 22665 22763 22861 22959 23057 23155 23253 23351 23449
##  [7105] 23547 23645 23743 23841 23939 24037 24135 24233 24331 24429 24527 24625
##  [7117] 24723 24821 24919 25017 25115 25213 25311 25409 25507 25605 25703 25801
##  [7129]    28   126   224   322   420   518   616   714   812   910  1008  1106
##  [7141]  1204  1302  1400  1498  1596  1694  1792  1890  1988  2086  2184  2282
##  [7153]  2380  2478  2576  2674  2772  2870  2968  3066  3164  3262  3360  3458
##  [7165]  3556  3654  3752  3850  3948  4046  4144  4242  4340  4438  4536  4634
##  [7177]  4732  4830  4928  5026  5124  5222  5320  5418  5516  5614  5712  5810
##  [7189]  5908  6006  6104  6202  6300  6398  6496  6594  6692  6790  6888  6986
##  [7201]  7084  7182  7280  7378  7476  7574  7672  7770  7868  7966  8064  8162
##  [7213]  8260  8358  8456  8554  8652  8750  8848  8946  9044  9142  9240  9338
##  [7225]  9436  9534  9632  9730  9828  9926 10024 10122 10220 10318 10416 10514
##  [7237] 10612 10710 10808 10906 11004 11102 11200 11298 11396 11494 11592 11690
##  [7249] 11788 11886 11984 12082 12180 12278 12376 12474 12572 12670 12768 12866
##  [7261] 12964 13062 13160 13258 13356 13454 13552 13650 13748 13846 13944 14042
##  [7273] 14140 14238 14336 14434 14532 14630 14728 14826 14924 15022 15120 15218
##  [7285] 15316 15414 15512 15610 15708 15806 15904 16002 16100 16198 16296 16394
##  [7297] 16492 16590 16688 16786 16884 16982 17080 17178 17276 17374 17472 17570
##  [7309] 17668 17766 17864 17962 18060 18158 18256 18354 18452 18550 18648 18746
##  [7321] 18844 18942 19040 19138 19236 19334 19432 19530 19628 19726 19824 19922
##  [7333] 20020 20118 20216 20314 20412 20510 20608 20706 20804 20902 21000 21098
##  [7345] 21196 21294 21392 21490 21588 21686 21784 21882 21980 22078 22176 22274
##  [7357] 22372 22470 22568 22666 22764 22862 22960 23058 23156 23254 23352 23450
##  [7369] 23548 23646 23744 23842 23940 24038 24136 24234 24332 24430 24528 24626
##  [7381] 24724 24822 24920 25018 25116 25214 25312 25410 25508 25606 25704 25802
##  [7393]    29   127   225   323   421   519   617   715   813   911  1009  1107
##  [7405]  1205  1303  1401  1499  1597  1695  1793  1891  1989  2087  2185  2283
##  [7417]  2381  2479  2577  2675  2773  2871  2969  3067  3165  3263  3361  3459
##  [7429]  3557  3655  3753  3851  3949  4047  4145  4243  4341  4439  4537  4635
##  [7441]  4733  4831  4929  5027  5125  5223  5321  5419  5517  5615  5713  5811
##  [7453]  5909  6007  6105  6203  6301  6399  6497  6595  6693  6791  6889  6987
##  [7465]  7085  7183  7281  7379  7477  7575  7673  7771  7869  7967  8065  8163
##  [7477]  8261  8359  8457  8555  8653  8751  8849  8947  9045  9143  9241  9339
##  [7489]  9437  9535  9633  9731  9829  9927 10025 10123 10221 10319 10417 10515
##  [7501] 10613 10711 10809 10907 11005 11103 11201 11299 11397 11495 11593 11691
##  [7513] 11789 11887 11985 12083 12181 12279 12377 12475 12573 12671 12769 12867
##  [7525] 12965 13063 13161 13259 13357 13455 13553 13651 13749 13847 13945 14043
##  [7537] 14141 14239 14337 14435 14533 14631 14729 14827 14925 15023 15121 15219
##  [7549] 15317 15415 15513 15611 15709 15807 15905 16003 16101 16199 16297 16395
##  [7561] 16493 16591 16689 16787 16885 16983 17081 17179 17277 17375 17473 17571
##  [7573] 17669 17767 17865 17963 18061 18159 18257 18355 18453 18551 18649 18747
##  [7585] 18845 18943 19041 19139 19237 19335 19433 19531 19629 19727 19825 19923
##  [7597] 20021 20119 20217 20315 20413 20511 20609 20707 20805 20903 21001 21099
##  [7609] 21197 21295 21393 21491 21589 21687 21785 21883 21981 22079 22177 22275
##  [7621] 22373 22471 22569 22667 22765 22863 22961 23059 23157 23255 23353 23451
##  [7633] 23549 23647 23745 23843 23941 24039 24137 24235 24333 24431 24529 24627
##  [7645] 24725 24823 24921 25019 25117 25215 25313 25411 25509 25607 25705 25803
##  [7657]    30   128   226   324   422   520   618   716   814   912  1010  1108
##  [7669]  1206  1304  1402  1500  1598  1696  1794  1892  1990  2088  2186  2284
##  [7681]  2382  2480  2578  2676  2774  2872  2970  3068  3166  3264  3362  3460
##  [7693]  3558  3656  3754  3852  3950  4048  4146  4244  4342  4440  4538  4636
##  [7705]  4734  4832  4930  5028  5126  5224  5322  5420  5518  5616  5714  5812
##  [7717]  5910  6008  6106  6204  6302  6400  6498  6596  6694  6792  6890  6988
##  [7729]  7086  7184  7282  7380  7478  7576  7674  7772  7870  7968  8066  8164
##  [7741]  8262  8360  8458  8556  8654  8752  8850  8948  9046  9144  9242  9340
##  [7753]  9438  9536  9634  9732  9830  9928 10026 10124 10222 10320 10418 10516
##  [7765] 10614 10712 10810 10908 11006 11104 11202 11300 11398 11496 11594 11692
##  [7777] 11790 11888 11986 12084 12182 12280 12378 12476 12574 12672 12770 12868
##  [7789] 12966 13064 13162 13260 13358 13456 13554 13652 13750 13848 13946 14044
##  [7801] 14142 14240 14338 14436 14534 14632 14730 14828 14926 15024 15122 15220
##  [7813] 15318 15416 15514 15612 15710 15808 15906 16004 16102 16200 16298 16396
##  [7825] 16494 16592 16690 16788 16886 16984 17082 17180 17278 17376 17474 17572
##  [7837] 17670 17768 17866 17964 18062 18160 18258 18356 18454 18552 18650 18748
##  [7849] 18846 18944 19042 19140 19238 19336 19434 19532 19630 19728 19826 19924
##  [7861] 20022 20120 20218 20316 20414 20512 20610 20708 20806 20904 21002 21100
##  [7873] 21198 21296 21394 21492 21590 21688 21786 21884 21982 22080 22178 22276
##  [7885] 22374 22472 22570 22668 22766 22864 22962 23060 23158 23256 23354 23452
##  [7897] 23550 23648 23746 23844 23942 24040 24138 24236 24334 24432 24530 24628
##  [7909] 24726 24824 24922 25020 25118 25216 25314 25412 25510 25608 25706 25804
##  [7921]    31   129   227   325   423   521   619   717   815   913  1011  1109
##  [7933]  1207  1305  1403  1501  1599  1697  1795  1893  1991  2089  2187  2285
##  [7945]  2383  2481  2579  2677  2775  2873  2971  3069  3167  3265  3363  3461
##  [7957]  3559  3657  3755  3853  3951  4049  4147  4245  4343  4441  4539  4637
##  [7969]  4735  4833  4931  5029  5127  5225  5323  5421  5519  5617  5715  5813
##  [7981]  5911  6009  6107  6205  6303  6401  6499  6597  6695  6793  6891  6989
##  [7993]  7087  7185  7283  7381  7479  7577  7675  7773  7871  7969  8067  8165
##  [8005]  8263  8361  8459  8557  8655  8753  8851  8949  9047  9145  9243  9341
##  [8017]  9439  9537  9635  9733  9831  9929 10027 10125 10223 10321 10419 10517
##  [8029] 10615 10713 10811 10909 11007 11105 11203 11301 11399 11497 11595 11693
##  [8041] 11791 11889 11987 12085 12183 12281 12379 12477 12575 12673 12771 12869
##  [8053] 12967 13065 13163 13261 13359 13457 13555 13653 13751 13849 13947 14045
##  [8065] 14143 14241 14339 14437 14535 14633 14731 14829 14927 15025 15123 15221
##  [8077] 15319 15417 15515 15613 15711 15809 15907 16005 16103 16201 16299 16397
##  [8089] 16495 16593 16691 16789 16887 16985 17083 17181 17279 17377 17475 17573
##  [8101] 17671 17769 17867 17965 18063 18161 18259 18357 18455 18553 18651 18749
##  [8113] 18847 18945 19043 19141 19239 19337 19435 19533 19631 19729 19827 19925
##  [8125] 20023 20121 20219 20317 20415 20513 20611 20709 20807 20905 21003 21101
##  [8137] 21199 21297 21395 21493 21591 21689 21787 21885 21983 22081 22179 22277
##  [8149] 22375 22473 22571 22669 22767 22865 22963 23061 23159 23257 23355 23453
##  [8161] 23551 23649 23747 23845 23943 24041 24139 24237 24335 24433 24531 24629
##  [8173] 24727 24825 24923 25021 25119 25217 25315 25413 25511 25609 25707 25805
##  [8185]    32   130   228   326   424   522   620   718   816   914  1012  1110
##  [8197]  1208  1306  1404  1502  1600  1698  1796  1894  1992  2090  2188  2286
##  [8209]  2384  2482  2580  2678  2776  2874  2972  3070  3168  3266  3364  3462
##  [8221]  3560  3658  3756  3854  3952  4050  4148  4246  4344  4442  4540  4638
##  [8233]  4736  4834  4932  5030  5128  5226  5324  5422  5520  5618  5716  5814
##  [8245]  5912  6010  6108  6206  6304  6402  6500  6598  6696  6794  6892  6990
##  [8257]  7088  7186  7284  7382  7480  7578  7676  7774  7872  7970  8068  8166
##  [8269]  8264  8362  8460  8558  8656  8754  8852  8950  9048  9146  9244  9342
##  [8281]  9440  9538  9636  9734  9832  9930 10028 10126 10224 10322 10420 10518
##  [8293] 10616 10714 10812 10910 11008 11106 11204 11302 11400 11498 11596 11694
##  [8305] 11792 11890 11988 12086 12184 12282 12380 12478 12576 12674 12772 12870
##  [8317] 12968 13066 13164 13262 13360 13458 13556 13654 13752 13850 13948 14046
##  [8329] 14144 14242 14340 14438 14536 14634 14732 14830 14928 15026 15124 15222
##  [8341] 15320 15418 15516 15614 15712 15810 15908 16006 16104 16202 16300 16398
##  [8353] 16496 16594 16692 16790 16888 16986 17084 17182 17280 17378 17476 17574
##  [8365] 17672 17770 17868 17966 18064 18162 18260 18358 18456 18554 18652 18750
##  [8377] 18848 18946 19044 19142 19240 19338 19436 19534 19632 19730 19828 19926
##  [8389] 20024 20122 20220 20318 20416 20514 20612 20710 20808 20906 21004 21102
##  [8401] 21200 21298 21396 21494 21592 21690 21788 21886 21984 22082 22180 22278
##  [8413] 22376 22474 22572 22670 22768 22866 22964 23062 23160 23258 23356 23454
##  [8425] 23552 23650 23748 23846 23944 24042 24140 24238 24336 24434 24532 24630
##  [8437] 24728 24826 24924 25022 25120 25218 25316 25414 25512 25610 25708 25806
##  [8449]    33   131   229   327   425   523   621   719   817   915  1013  1111
##  [8461]  1209  1307  1405  1503  1601  1699  1797  1895  1993  2091  2189  2287
##  [8473]  2385  2483  2581  2679  2777  2875  2973  3071  3169  3267  3365  3463
##  [8485]  3561  3659  3757  3855  3953  4051  4149  4247  4345  4443  4541  4639
##  [8497]  4737  4835  4933  5031  5129  5227  5325  5423  5521  5619  5717  5815
##  [8509]  5913  6011  6109  6207  6305  6403  6501  6599  6697  6795  6893  6991
##  [8521]  7089  7187  7285  7383  7481  7579  7677  7775  7873  7971  8069  8167
##  [8533]  8265  8363  8461  8559  8657  8755  8853  8951  9049  9147  9245  9343
##  [8545]  9441  9539  9637  9735  9833  9931 10029 10127 10225 10323 10421 10519
##  [8557] 10617 10715 10813 10911 11009 11107 11205 11303 11401 11499 11597 11695
##  [8569] 11793 11891 11989 12087 12185 12283 12381 12479 12577 12675 12773 12871
##  [8581] 12969 13067 13165 13263 13361 13459 13557 13655 13753 13851 13949 14047
##  [8593] 14145 14243 14341 14439 14537 14635 14733 14831 14929 15027 15125 15223
##  [8605] 15321 15419 15517 15615 15713 15811 15909 16007 16105 16203 16301 16399
##  [8617] 16497 16595 16693 16791 16889 16987 17085 17183 17281 17379 17477 17575
##  [8629] 17673 17771 17869 17967 18065 18163 18261 18359 18457 18555 18653 18751
##  [8641] 18849 18947 19045 19143 19241 19339 19437 19535 19633 19731 19829 19927
##  [8653] 20025 20123 20221 20319 20417 20515 20613 20711 20809 20907 21005 21103
##  [8665] 21201 21299 21397 21495 21593 21691 21789 21887 21985 22083 22181 22279
##  [8677] 22377 22475 22573 22671 22769 22867 22965 23063 23161 23259 23357 23455
##  [8689] 23553 23651 23749 23847 23945 24043 24141 24239 24337 24435 24533 24631
##  [8701] 24729 24827 24925 25023 25121 25219 25317 25415 25513 25611 25709 25807
##  [8713]    34   132   230   328   426   524   622   720   818   916  1014  1112
##  [8725]  1210  1308  1406  1504  1602  1700  1798  1896  1994  2092  2190  2288
##  [8737]  2386  2484  2582  2680  2778  2876  2974  3072  3170  3268  3366  3464
##  [8749]  3562  3660  3758  3856  3954  4052  4150  4248  4346  4444  4542  4640
##  [8761]  4738  4836  4934  5032  5130  5228  5326  5424  5522  5620  5718  5816
##  [8773]  5914  6012  6110  6208  6306  6404  6502  6600  6698  6796  6894  6992
##  [8785]  7090  7188  7286  7384  7482  7580  7678  7776  7874  7972  8070  8168
##  [8797]  8266  8364  8462  8560  8658  8756  8854  8952  9050  9148  9246  9344
##  [8809]  9442  9540  9638  9736  9834  9932 10030 10128 10226 10324 10422 10520
##  [8821] 10618 10716 10814 10912 11010 11108 11206 11304 11402 11500 11598 11696
##  [8833] 11794 11892 11990 12088 12186 12284 12382 12480 12578 12676 12774 12872
##  [8845] 12970 13068 13166 13264 13362 13460 13558 13656 13754 13852 13950 14048
##  [8857] 14146 14244 14342 14440 14538 14636 14734 14832 14930 15028 15126 15224
##  [8869] 15322 15420 15518 15616 15714 15812 15910 16008 16106 16204 16302 16400
##  [8881] 16498 16596 16694 16792 16890 16988 17086 17184 17282 17380 17478 17576
##  [8893] 17674 17772 17870 17968 18066 18164 18262 18360 18458 18556 18654 18752
##  [8905] 18850 18948 19046 19144 19242 19340 19438 19536 19634 19732 19830 19928
##  [8917] 20026 20124 20222 20320 20418 20516 20614 20712 20810 20908 21006 21104
##  [8929] 21202 21300 21398 21496 21594 21692 21790 21888 21986 22084 22182 22280
##  [8941] 22378 22476 22574 22672 22770 22868 22966 23064 23162 23260 23358 23456
##  [8953] 23554 23652 23750 23848 23946 24044 24142 24240 24338 24436 24534 24632
##  [8965] 24730 24828 24926 25024 25122 25220 25318 25416 25514 25612 25710 25808
##  [8977]    35   133   231   329   427   525   623   721   819   917  1015  1113
##  [8989]  1211  1309  1407  1505  1603  1701  1799  1897  1995  2093  2191  2289
##  [9001]  2387  2485  2583  2681  2779  2877  2975  3073  3171  3269  3367  3465
##  [9013]  3563  3661  3759  3857  3955  4053  4151  4249  4347  4445  4543  4641
##  [9025]  4739  4837  4935  5033  5131  5229  5327  5425  5523  5621  5719  5817
##  [9037]  5915  6013  6111  6209  6307  6405  6503  6601  6699  6797  6895  6993
##  [9049]  7091  7189  7287  7385  7483  7581  7679  7777  7875  7973  8071  8169
##  [9061]  8267  8365  8463  8561  8659  8757  8855  8953  9051  9149  9247  9345
##  [9073]  9443  9541  9639  9737  9835  9933 10031 10129 10227 10325 10423 10521
##  [9085] 10619 10717 10815 10913 11011 11109 11207 11305 11403 11501 11599 11697
##  [9097] 11795 11893 11991 12089 12187 12285 12383 12481 12579 12677 12775 12873
##  [9109] 12971 13069 13167 13265 13363 13461 13559 13657 13755 13853 13951 14049
##  [9121] 14147 14245 14343 14441 14539 14637 14735 14833 14931 15029 15127 15225
##  [9133] 15323 15421 15519 15617 15715 15813 15911 16009 16107 16205 16303 16401
##  [9145] 16499 16597 16695 16793 16891 16989 17087 17185 17283 17381 17479 17577
##  [9157] 17675 17773 17871 17969 18067 18165 18263 18361 18459 18557 18655 18753
##  [9169] 18851 18949 19047 19145 19243 19341 19439 19537 19635 19733 19831 19929
##  [9181] 20027 20125 20223 20321 20419 20517 20615 20713 20811 20909 21007 21105
##  [9193] 21203 21301 21399 21497 21595 21693 21791 21889 21987 22085 22183 22281
##  [9205] 22379 22477 22575 22673 22771 22869 22967 23065 23163 23261 23359 23457
##  [9217] 23555 23653 23751 23849 23947 24045 24143 24241 24339 24437 24535 24633
##  [9229] 24731 24829 24927 25025 25123 25221 25319 25417 25515 25613 25711 25809
##  [9241]    36   134   232   330   428   526   624   722   820   918  1016  1114
##  [9253]  1212  1310  1408  1506  1604  1702  1800  1898  1996  2094  2192  2290
##  [9265]  2388  2486  2584  2682  2780  2878  2976  3074  3172  3270  3368  3466
##  [9277]  3564  3662  3760  3858  3956  4054  4152  4250  4348  4446  4544  4642
##  [9289]  4740  4838  4936  5034  5132  5230  5328  5426  5524  5622  5720  5818
##  [9301]  5916  6014  6112  6210  6308  6406  6504  6602  6700  6798  6896  6994
##  [9313]  7092  7190  7288  7386  7484  7582  7680  7778  7876  7974  8072  8170
##  [9325]  8268  8366  8464  8562  8660  8758  8856  8954  9052  9150  9248  9346
##  [9337]  9444  9542  9640  9738  9836  9934 10032 10130 10228 10326 10424 10522
##  [9349] 10620 10718 10816 10914 11012 11110 11208 11306 11404 11502 11600 11698
##  [9361] 11796 11894 11992 12090 12188 12286 12384 12482 12580 12678 12776 12874
##  [9373] 12972 13070 13168 13266 13364 13462 13560 13658 13756 13854 13952 14050
##  [9385] 14148 14246 14344 14442 14540 14638 14736 14834 14932 15030 15128 15226
##  [9397] 15324 15422 15520 15618 15716 15814 15912 16010 16108 16206 16304 16402
##  [9409] 16500 16598 16696 16794 16892 16990 17088 17186 17284 17382 17480 17578
##  [9421] 17676 17774 17872 17970 18068 18166 18264 18362 18460 18558 18656 18754
##  [9433] 18852 18950 19048 19146 19244 19342 19440 19538 19636 19734 19832 19930
##  [9445] 20028 20126 20224 20322 20420 20518 20616 20714 20812 20910 21008 21106
##  [9457] 21204 21302 21400 21498 21596 21694 21792 21890 21988 22086 22184 22282
##  [9469] 22380 22478 22576 22674 22772 22870 22968 23066 23164 23262 23360 23458
##  [9481] 23556 23654 23752 23850 23948 24046 24144 24242 24340 24438 24536 24634
##  [9493] 24732 24830 24928 25026 25124 25222 25320 25418 25516 25614 25712 25810
##  [9505]    37   135   233   331   429   527   625   723   821   919  1017  1115
##  [9517]  1213  1311  1409  1507  1605  1703  1801  1899  1997  2095  2193  2291
##  [9529]  2389  2487  2585  2683  2781  2879  2977  3075  3173  3271  3369  3467
##  [9541]  3565  3663  3761  3859  3957  4055  4153  4251  4349  4447  4545  4643
##  [9553]  4741  4839  4937  5035  5133  5231  5329  5427  5525  5623  5721  5819
##  [9565]  5917  6015  6113  6211  6309  6407  6505  6603  6701  6799  6897  6995
##  [9577]  7093  7191  7289  7387  7485  7583  7681  7779  7877  7975  8073  8171
##  [9589]  8269  8367  8465  8563  8661  8759  8857  8955  9053  9151  9249  9347
##  [9601]  9445  9543  9641  9739  9837  9935 10033 10131 10229 10327 10425 10523
##  [9613] 10621 10719 10817 10915 11013 11111 11209 11307 11405 11503 11601 11699
##  [9625] 11797 11895 11993 12091 12189 12287 12385 12483 12581 12679 12777 12875
##  [9637] 12973 13071 13169 13267 13365 13463 13561 13659 13757 13855 13953 14051
##  [9649] 14149 14247 14345 14443 14541 14639 14737 14835 14933 15031 15129 15227
##  [9661] 15325 15423 15521 15619 15717 15815 15913 16011 16109 16207 16305 16403
##  [9673] 16501 16599 16697 16795 16893 16991 17089 17187 17285 17383 17481 17579
##  [9685] 17677 17775 17873 17971 18069 18167 18265 18363 18461 18559 18657 18755
##  [9697] 18853 18951 19049 19147 19245 19343 19441 19539 19637 19735 19833 19931
##  [9709] 20029 20127 20225 20323 20421 20519 20617 20715 20813 20911 21009 21107
##  [9721] 21205 21303 21401 21499 21597 21695 21793 21891 21989 22087 22185 22283
##  [9733] 22381 22479 22577 22675 22773 22871 22969 23067 23165 23263 23361 23459
##  [9745] 23557 23655 23753 23851 23949 24047 24145 24243 24341 24439 24537 24635
##  [9757] 24733 24831 24929 25027 25125 25223 25321 25419 25517 25615 25713 25811
##  [9769]    38   136   234   332   430   528   626   724   822   920  1018  1116
##  [9781]  1214  1312  1410  1508  1606  1704  1802  1900  1998  2096  2194  2292
##  [9793]  2390  2488  2586  2684  2782  2880  2978  3076  3174  3272  3370  3468
##  [9805]  3566  3664  3762  3860  3958  4056  4154  4252  4350  4448  4546  4644
##  [9817]  4742  4840  4938  5036  5134  5232  5330  5428  5526  5624  5722  5820
##  [9829]  5918  6016  6114  6212  6310  6408  6506  6604  6702  6800  6898  6996
##  [9841]  7094  7192  7290  7388  7486  7584  7682  7780  7878  7976  8074  8172
##  [9853]  8270  8368  8466  8564  8662  8760  8858  8956  9054  9152  9250  9348
##  [9865]  9446  9544  9642  9740  9838  9936 10034 10132 10230 10328 10426 10524
##  [9877] 10622 10720 10818 10916 11014 11112 11210 11308 11406 11504 11602 11700
##  [9889] 11798 11896 11994 12092 12190 12288 12386 12484 12582 12680 12778 12876
##  [9901] 12974 13072 13170 13268 13366 13464 13562 13660 13758 13856 13954 14052
##  [9913] 14150 14248 14346 14444 14542 14640 14738 14836 14934 15032 15130 15228
##  [9925] 15326 15424 15522 15620 15718 15816 15914 16012 16110 16208 16306 16404
##  [9937] 16502 16600 16698 16796 16894 16992 17090 17188 17286 17384 17482 17580
##  [9949] 17678 17776 17874 17972 18070 18168 18266 18364 18462 18560 18658 18756
##  [9961] 18854 18952 19050 19148 19246 19344 19442 19540 19638 19736 19834 19932
##  [9973] 20030 20128 20226 20324 20422 20520 20618 20716 20814 20912 21010 21108
##  [9985] 21206 21304 21402 21500 21598 21696 21794 21892 21990 22088 22186 22284
##  [9997] 22382 22480 22578 22676 22774 22872 22970 23068 23166 23264 23362 23460
## [10009] 23558 23656 23754 23852 23950 24048 24146 24244 24342 24440 24538 24636
## [10021] 24734 24832 24930 25028 25126 25224 25322 25420 25518 25616 25714 25812
## [10033]    39   137   235   333   431   529   627   725   823   921  1019  1117
## [10045]  1215  1313  1411  1509  1607  1705  1803  1901  1999  2097  2195  2293
## [10057]  2391  2489  2587  2685  2783  2881  2979  3077  3175  3273  3371  3469
## [10069]  3567  3665  3763  3861  3959  4057  4155  4253  4351  4449  4547  4645
## [10081]  4743  4841  4939  5037  5135  5233  5331  5429  5527  5625  5723  5821
## [10093]  5919  6017  6115  6213  6311  6409  6507  6605  6703  6801  6899  6997
## [10105]  7095  7193  7291  7389  7487  7585  7683  7781  7879  7977  8075  8173
## [10117]  8271  8369  8467  8565  8663  8761  8859  8957  9055  9153  9251  9349
## [10129]  9447  9545  9643  9741  9839  9937 10035 10133 10231 10329 10427 10525
## [10141] 10623 10721 10819 10917 11015 11113 11211 11309 11407 11505 11603 11701
## [10153] 11799 11897 11995 12093 12191 12289 12387 12485 12583 12681 12779 12877
## [10165] 12975 13073 13171 13269 13367 13465 13563 13661 13759 13857 13955 14053
## [10177] 14151 14249 14347 14445 14543 14641 14739 14837 14935 15033 15131 15229
## [10189] 15327 15425 15523 15621 15719 15817 15915 16013 16111 16209 16307 16405
## [10201] 16503 16601 16699 16797 16895 16993 17091 17189 17287 17385 17483 17581
## [10213] 17679 17777 17875 17973 18071 18169 18267 18365 18463 18561 18659 18757
## [10225] 18855 18953 19051 19149 19247 19345 19443 19541 19639 19737 19835 19933
## [10237] 20031 20129 20227 20325 20423 20521 20619 20717 20815 20913 21011 21109
## [10249] 21207 21305 21403 21501 21599 21697 21795 21893 21991 22089 22187 22285
## [10261] 22383 22481 22579 22677 22775 22873 22971 23069 23167 23265 23363 23461
## [10273] 23559 23657 23755 23853 23951 24049 24147 24245 24343 24441 24539 24637
## [10285] 24735 24833 24931 25029 25127 25225 25323 25421 25519 25617 25715 25813
## [10297]    40   138   236   334   432   530   628   726   824   922  1020  1118
## [10309]  1216  1314  1412  1510  1608  1706  1804  1902  2000  2098  2196  2294
## [10321]  2392  2490  2588  2686  2784  2882  2980  3078  3176  3274  3372  3470
## [10333]  3568  3666  3764  3862  3960  4058  4156  4254  4352  4450  4548  4646
## [10345]  4744  4842  4940  5038  5136  5234  5332  5430  5528  5626  5724  5822
## [10357]  5920  6018  6116  6214  6312  6410  6508  6606  6704  6802  6900  6998
## [10369]  7096  7194  7292  7390  7488  7586  7684  7782  7880  7978  8076  8174
## [10381]  8272  8370  8468  8566  8664  8762  8860  8958  9056  9154  9252  9350
## [10393]  9448  9546  9644  9742  9840  9938 10036 10134 10232 10330 10428 10526
## [10405] 10624 10722 10820 10918 11016 11114 11212 11310 11408 11506 11604 11702
## [10417] 11800 11898 11996 12094 12192 12290 12388 12486 12584 12682 12780 12878
## [10429] 12976 13074 13172 13270 13368 13466 13564 13662 13760 13858 13956 14054
## [10441] 14152 14250 14348 14446 14544 14642 14740 14838 14936 15034 15132 15230
## [10453] 15328 15426 15524 15622 15720 15818 15916 16014 16112 16210 16308 16406
## [10465] 16504 16602 16700 16798 16896 16994 17092 17190 17288 17386 17484 17582
## [10477] 17680 17778 17876 17974 18072 18170 18268 18366 18464 18562 18660 18758
## [10489] 18856 18954 19052 19150 19248 19346 19444 19542 19640 19738 19836 19934
## [10501] 20032 20130 20228 20326 20424 20522 20620 20718 20816 20914 21012 21110
## [10513] 21208 21306 21404 21502 21600 21698 21796 21894 21992 22090 22188 22286
## [10525] 22384 22482 22580 22678 22776 22874 22972 23070 23168 23266 23364 23462
## [10537] 23560 23658 23756 23854 23952 24050 24148 24246 24344 24442 24540 24638
## [10549] 24736 24834 24932 25030 25128 25226 25324 25422 25520 25618 25716 25814
## [10561]    41   139   237   335   433   531   629   727   825   923  1021  1119
## [10573]  1217  1315  1413  1511  1609  1707  1805  1903  2001  2099  2197  2295
## [10585]  2393  2491  2589  2687  2785  2883  2981  3079  3177  3275  3373  3471
## [10597]  3569  3667  3765  3863  3961  4059  4157  4255  4353  4451  4549  4647
## [10609]  4745  4843  4941  5039  5137  5235  5333  5431  5529  5627  5725  5823
## [10621]  5921  6019  6117  6215  6313  6411  6509  6607  6705  6803  6901  6999
## [10633]  7097  7195  7293  7391  7489  7587  7685  7783  7881  7979  8077  8175
## [10645]  8273  8371  8469  8567  8665  8763  8861  8959  9057  9155  9253  9351
## [10657]  9449  9547  9645  9743  9841  9939 10037 10135 10233 10331 10429 10527
## [10669] 10625 10723 10821 10919 11017 11115 11213 11311 11409 11507 11605 11703
## [10681] 11801 11899 11997 12095 12193 12291 12389 12487 12585 12683 12781 12879
## [10693] 12977 13075 13173 13271 13369 13467 13565 13663 13761 13859 13957 14055
## [10705] 14153 14251 14349 14447 14545 14643 14741 14839 14937 15035 15133 15231
## [10717] 15329 15427 15525 15623 15721 15819 15917 16015 16113 16211 16309 16407
## [10729] 16505 16603 16701 16799 16897 16995 17093 17191 17289 17387 17485 17583
## [10741] 17681 17779 17877 17975 18073 18171 18269 18367 18465 18563 18661 18759
## [10753] 18857 18955 19053 19151 19249 19347 19445 19543 19641 19739 19837 19935
## [10765] 20033 20131 20229 20327 20425 20523 20621 20719 20817 20915 21013 21111
## [10777] 21209 21307 21405 21503 21601 21699 21797 21895 21993 22091 22189 22287
## [10789] 22385 22483 22581 22679 22777 22875 22973 23071 23169 23267 23365 23463
## [10801] 23561 23659 23757 23855 23953 24051 24149 24247 24345 24443 24541 24639
## [10813] 24737 24835 24933 25031 25129 25227 25325 25423 25521 25619 25717 25815
## [10825]    42   140   238   336   434   532   630   728   826   924  1022  1120
## [10837]  1218  1316  1414  1512  1610  1708  1806  1904  2002  2100  2198  2296
## [10849]  2394  2492  2590  2688  2786  2884  2982  3080  3178  3276  3374  3472
## [10861]  3570  3668  3766  3864  3962  4060  4158  4256  4354  4452  4550  4648
## [10873]  4746  4844  4942  5040  5138  5236  5334  5432  5530  5628  5726  5824
## [10885]  5922  6020  6118  6216  6314  6412  6510  6608  6706  6804  6902  7000
## [10897]  7098  7196  7294  7392  7490  7588  7686  7784  7882  7980  8078  8176
## [10909]  8274  8372  8470  8568  8666  8764  8862  8960  9058  9156  9254  9352
## [10921]  9450  9548  9646  9744  9842  9940 10038 10136 10234 10332 10430 10528
## [10933] 10626 10724 10822 10920 11018 11116 11214 11312 11410 11508 11606 11704
## [10945] 11802 11900 11998 12096 12194 12292 12390 12488 12586 12684 12782 12880
## [10957] 12978 13076 13174 13272 13370 13468 13566 13664 13762 13860 13958 14056
## [10969] 14154 14252 14350 14448 14546 14644 14742 14840 14938 15036 15134 15232
## [10981] 15330 15428 15526 15624 15722 15820 15918 16016 16114 16212 16310 16408
## [10993] 16506 16604 16702 16800 16898 16996 17094 17192 17290 17388 17486 17584
## [11005] 17682 17780 17878 17976 18074 18172 18270 18368 18466 18564 18662 18760
## [11017] 18858 18956 19054 19152 19250 19348 19446 19544 19642 19740 19838 19936
## [11029] 20034 20132 20230 20328 20426 20524 20622 20720 20818 20916 21014 21112
## [11041] 21210 21308 21406 21504 21602 21700 21798 21896 21994 22092 22190 22288
## [11053] 22386 22484 22582 22680 22778 22876 22974 23072 23170 23268 23366 23464
## [11065] 23562 23660 23758 23856 23954 24052 24150 24248 24346 24444 24542 24640
## [11077] 24738 24836 24934 25032 25130 25228 25326 25424 25522 25620 25718 25816
## [11089]    43   141   239   337   435   533   631   729   827   925  1023  1121
## [11101]  1219  1317  1415  1513  1611  1709  1807  1905  2003  2101  2199  2297
## [11113]  2395  2493  2591  2689  2787  2885  2983  3081  3179  3277  3375  3473
## [11125]  3571  3669  3767  3865  3963  4061  4159  4257  4355  4453  4551  4649
## [11137]  4747  4845  4943  5041  5139  5237  5335  5433  5531  5629  5727  5825
## [11149]  5923  6021  6119  6217  6315  6413  6511  6609  6707  6805  6903  7001
## [11161]  7099  7197  7295  7393  7491  7589  7687  7785  7883  7981  8079  8177
## [11173]  8275  8373  8471  8569  8667  8765  8863  8961  9059  9157  9255  9353
## [11185]  9451  9549  9647  9745  9843  9941 10039 10137 10235 10333 10431 10529
## [11197] 10627 10725 10823 10921 11019 11117 11215 11313 11411 11509 11607 11705
## [11209] 11803 11901 11999 12097 12195 12293 12391 12489 12587 12685 12783 12881
## [11221] 12979 13077 13175 13273 13371 13469 13567 13665 13763 13861 13959 14057
## [11233] 14155 14253 14351 14449 14547 14645 14743 14841 14939 15037 15135 15233
## [11245] 15331 15429 15527 15625 15723 15821 15919 16017 16115 16213 16311 16409
## [11257] 16507 16605 16703 16801 16899 16997 17095 17193 17291 17389 17487 17585
## [11269] 17683 17781 17879 17977 18075 18173 18271 18369 18467 18565 18663 18761
## [11281] 18859 18957 19055 19153 19251 19349 19447 19545 19643 19741 19839 19937
## [11293] 20035 20133 20231 20329 20427 20525 20623 20721 20819 20917 21015 21113
## [11305] 21211 21309 21407 21505 21603 21701 21799 21897 21995 22093 22191 22289
## [11317] 22387 22485 22583 22681 22779 22877 22975 23073 23171 23269 23367 23465
## [11329] 23563 23661 23759 23857 23955 24053 24151 24249 24347 24445 24543 24641
## [11341] 24739 24837 24935 25033 25131 25229 25327 25425 25523 25621 25719 25817
## [11353]    44   142   240   338   436   534   632   730   828   926  1024  1122
## [11365]  1220  1318  1416  1514  1612  1710  1808  1906  2004  2102  2200  2298
## [11377]  2396  2494  2592  2690  2788  2886  2984  3082  3180  3278  3376  3474
## [11389]  3572  3670  3768  3866  3964  4062  4160  4258  4356  4454  4552  4650
## [11401]  4748  4846  4944  5042  5140  5238  5336  5434  5532  5630  5728  5826
## [11413]  5924  6022  6120  6218  6316  6414  6512  6610  6708  6806  6904  7002
## [11425]  7100  7198  7296  7394  7492  7590  7688  7786  7884  7982  8080  8178
## [11437]  8276  8374  8472  8570  8668  8766  8864  8962  9060  9158  9256  9354
## [11449]  9452  9550  9648  9746  9844  9942 10040 10138 10236 10334 10432 10530
## [11461] 10628 10726 10824 10922 11020 11118 11216 11314 11412 11510 11608 11706
## [11473] 11804 11902 12000 12098 12196 12294 12392 12490 12588 12686 12784 12882
## [11485] 12980 13078 13176 13274 13372 13470 13568 13666 13764 13862 13960 14058
## [11497] 14156 14254 14352 14450 14548 14646 14744 14842 14940 15038 15136 15234
## [11509] 15332 15430 15528 15626 15724 15822 15920 16018 16116 16214 16312 16410
## [11521] 16508 16606 16704 16802 16900 16998 17096 17194 17292 17390 17488 17586
## [11533] 17684 17782 17880 17978 18076 18174 18272 18370 18468 18566 18664 18762
## [11545] 18860 18958 19056 19154 19252 19350 19448 19546 19644 19742 19840 19938
## [11557] 20036 20134 20232 20330 20428 20526 20624 20722 20820 20918 21016 21114
## [11569] 21212 21310 21408 21506 21604 21702 21800 21898 21996 22094 22192 22290
## [11581] 22388 22486 22584 22682 22780 22878 22976 23074 23172 23270 23368 23466
## [11593] 23564 23662 23760 23858 23956 24054 24152 24250 24348 24446 24544 24642
## [11605] 24740 24838 24936 25034 25132 25230 25328 25426 25524 25622 25720 25818
## [11617]    45   143   241   339   437   535   633   731   829   927  1025  1123
## [11629]  1221  1319  1417  1515  1613  1711  1809  1907  2005  2103  2201  2299
## [11641]  2397  2495  2593  2691  2789  2887  2985  3083  3181  3279  3377  3475
## [11653]  3573  3671  3769  3867  3965  4063  4161  4259  4357  4455  4553  4651
## [11665]  4749  4847  4945  5043  5141  5239  5337  5435  5533  5631  5729  5827
## [11677]  5925  6023  6121  6219  6317  6415  6513  6611  6709  6807  6905  7003
## [11689]  7101  7199  7297  7395  7493  7591  7689  7787  7885  7983  8081  8179
## [11701]  8277  8375  8473  8571  8669  8767  8865  8963  9061  9159  9257  9355
## [11713]  9453  9551  9649  9747  9845  9943 10041 10139 10237 10335 10433 10531
## [11725] 10629 10727 10825 10923 11021 11119 11217 11315 11413 11511 11609 11707
## [11737] 11805 11903 12001 12099 12197 12295 12393 12491 12589 12687 12785 12883
## [11749] 12981 13079 13177 13275 13373 13471 13569 13667 13765 13863 13961 14059
## [11761] 14157 14255 14353 14451 14549 14647 14745 14843 14941 15039 15137 15235
## [11773] 15333 15431 15529 15627 15725 15823 15921 16019 16117 16215 16313 16411
## [11785] 16509 16607 16705 16803 16901 16999 17097 17195 17293 17391 17489 17587
## [11797] 17685 17783 17881 17979 18077 18175 18273 18371 18469 18567 18665 18763
## [11809] 18861 18959 19057 19155 19253 19351 19449 19547 19645 19743 19841 19939
## [11821] 20037 20135 20233 20331 20429 20527 20625 20723 20821 20919 21017 21115
## [11833] 21213 21311 21409 21507 21605 21703 21801 21899 21997 22095 22193 22291
## [11845] 22389 22487 22585 22683 22781 22879 22977 23075 23173 23271 23369 23467
## [11857] 23565 23663 23761 23859 23957 24055 24153 24251 24349 24447 24545 24643
## [11869] 24741 24839 24937 25035 25133 25231 25329 25427 25525 25623 25721 25819
## [11881]    46   144   242   340   438   536   634   732   830   928  1026  1124
## [11893]  1222  1320  1418  1516  1614  1712  1810  1908  2006  2104  2202  2300
## [11905]  2398  2496  2594  2692  2790  2888  2986  3084  3182  3280  3378  3476
## [11917]  3574  3672  3770  3868  3966  4064  4162  4260  4358  4456  4554  4652
## [11929]  4750  4848  4946  5044  5142  5240  5338  5436  5534  5632  5730  5828
## [11941]  5926  6024  6122  6220  6318  6416  6514  6612  6710  6808  6906  7004
## [11953]  7102  7200  7298  7396  7494  7592  7690  7788  7886  7984  8082  8180
## [11965]  8278  8376  8474  8572  8670  8768  8866  8964  9062  9160  9258  9356
## [11977]  9454  9552  9650  9748  9846  9944 10042 10140 10238 10336 10434 10532
## [11989] 10630 10728 10826 10924 11022 11120 11218 11316 11414 11512 11610 11708
## [12001] 11806 11904 12002 12100 12198 12296 12394 12492 12590 12688 12786 12884
## [12013] 12982 13080 13178 13276 13374 13472 13570 13668 13766 13864 13962 14060
## [12025] 14158 14256 14354 14452 14550 14648 14746 14844 14942 15040 15138 15236
## [12037] 15334 15432 15530 15628 15726 15824 15922 16020 16118 16216 16314 16412
## [12049] 16510 16608 16706 16804 16902 17000 17098 17196 17294 17392 17490 17588
## [12061] 17686 17784 17882 17980 18078 18176 18274 18372 18470 18568 18666 18764
## [12073] 18862 18960 19058 19156 19254 19352 19450 19548 19646 19744 19842 19940
## [12085] 20038 20136 20234 20332 20430 20528 20626 20724 20822 20920 21018 21116
## [12097] 21214 21312 21410 21508 21606 21704 21802 21900 21998 22096 22194 22292
## [12109] 22390 22488 22586 22684 22782 22880 22978 23076 23174 23272 23370 23468
## [12121] 23566 23664 23762 23860 23958 24056 24154 24252 24350 24448 24546 24644
## [12133] 24742 24840 24938 25036 25134 25232 25330 25428 25526 25624 25722 25820
## [12145]    47   145   243   341   439   537   635   733   831   929  1027  1125
## [12157]  1223  1321  1419  1517  1615  1713  1811  1909  2007  2105  2203  2301
## [12169]  2399  2497  2595  2693  2791  2889  2987  3085  3183  3281  3379  3477
## [12181]  3575  3673  3771  3869  3967  4065  4163  4261  4359  4457  4555  4653
## [12193]  4751  4849  4947  5045  5143  5241  5339  5437  5535  5633  5731  5829
## [12205]  5927  6025  6123  6221  6319  6417  6515  6613  6711  6809  6907  7005
## [12217]  7103  7201  7299  7397  7495  7593  7691  7789  7887  7985  8083  8181
## [12229]  8279  8377  8475  8573  8671  8769  8867  8965  9063  9161  9259  9357
## [12241]  9455  9553  9651  9749  9847  9945 10043 10141 10239 10337 10435 10533
## [12253] 10631 10729 10827 10925 11023 11121 11219 11317 11415 11513 11611 11709
## [12265] 11807 11905 12003 12101 12199 12297 12395 12493 12591 12689 12787 12885
## [12277] 12983 13081 13179 13277 13375 13473 13571 13669 13767 13865 13963 14061
## [12289] 14159 14257 14355 14453 14551 14649 14747 14845 14943 15041 15139 15237
## [12301] 15335 15433 15531 15629 15727 15825 15923 16021 16119 16217 16315 16413
## [12313] 16511 16609 16707 16805 16903 17001 17099 17197 17295 17393 17491 17589
## [12325] 17687 17785 17883 17981 18079 18177 18275 18373 18471 18569 18667 18765
## [12337] 18863 18961 19059 19157 19255 19353 19451 19549 19647 19745 19843 19941
## [12349] 20039 20137 20235 20333 20431 20529 20627 20725 20823 20921 21019 21117
## [12361] 21215 21313 21411 21509 21607 21705 21803 21901 21999 22097 22195 22293
## [12373] 22391 22489 22587 22685 22783 22881 22979 23077 23175 23273 23371 23469
## [12385] 23567 23665 23763 23861 23959 24057 24155 24253 24351 24449 24547 24645
## [12397] 24743 24841 24939 25037 25135 25233 25331 25429 25527 25625 25723 25821
## [12409]    48   146   244   342   440   538   636   734   832   930  1028  1126
## [12421]  1224  1322  1420  1518  1616  1714  1812  1910  2008  2106  2204  2302
## [12433]  2400  2498  2596  2694  2792  2890  2988  3086  3184  3282  3380  3478
## [12445]  3576  3674  3772  3870  3968  4066  4164  4262  4360  4458  4556  4654
## [12457]  4752  4850  4948  5046  5144  5242  5340  5438  5536  5634  5732  5830
## [12469]  5928  6026  6124  6222  6320  6418  6516  6614  6712  6810  6908  7006
## [12481]  7104  7202  7300  7398  7496  7594  7692  7790  7888  7986  8084  8182
## [12493]  8280  8378  8476  8574  8672  8770  8868  8966  9064  9162  9260  9358
## [12505]  9456  9554  9652  9750  9848  9946 10044 10142 10240 10338 10436 10534
## [12517] 10632 10730 10828 10926 11024 11122 11220 11318 11416 11514 11612 11710
## [12529] 11808 11906 12004 12102 12200 12298 12396 12494 12592 12690 12788 12886
## [12541] 12984 13082 13180 13278 13376 13474 13572 13670 13768 13866 13964 14062
## [12553] 14160 14258 14356 14454 14552 14650 14748 14846 14944 15042 15140 15238
## [12565] 15336 15434 15532 15630 15728 15826 15924 16022 16120 16218 16316 16414
## [12577] 16512 16610 16708 16806 16904 17002 17100 17198 17296 17394 17492 17590
## [12589] 17688 17786 17884 17982 18080 18178 18276 18374 18472 18570 18668 18766
## [12601] 18864 18962 19060 19158 19256 19354 19452 19550 19648 19746 19844 19942
## [12613] 20040 20138 20236 20334 20432 20530 20628 20726 20824 20922 21020 21118
## [12625] 21216 21314 21412 21510 21608 21706 21804 21902 22000 22098 22196 22294
## [12637] 22392 22490 22588 22686 22784 22882 22980 23078 23176 23274 23372 23470
## [12649] 23568 23666 23764 23862 23960 24058 24156 24254 24352 24450 24548 24646
## [12661] 24744 24842 24940 25038 25136 25234 25332 25430 25528 25626 25724 25822
## [12673]    49   147   245   343   441   539   637   735   833   931  1029  1127
## [12685]  1225  1323  1421  1519  1617  1715  1813  1911  2009  2107  2205  2303
## [12697]  2401  2499  2597  2695  2793  2891  2989  3087  3185  3283  3381  3479
## [12709]  3577  3675  3773  3871  3969  4067  4165  4263  4361  4459  4557  4655
## [12721]  4753  4851  4949  5047  5145  5243  5341  5439  5537  5635  5733  5831
## [12733]  5929  6027  6125  6223  6321  6419  6517  6615  6713  6811  6909  7007
## [12745]  7105  7203  7301  7399  7497  7595  7693  7791  7889  7987  8085  8183
## [12757]  8281  8379  8477  8575  8673  8771  8869  8967  9065  9163  9261  9359
## [12769]  9457  9555  9653  9751  9849  9947 10045 10143 10241 10339 10437 10535
## [12781] 10633 10731 10829 10927 11025 11123 11221 11319 11417 11515 11613 11711
## [12793] 11809 11907 12005 12103 12201 12299 12397 12495 12593 12691 12789 12887
## [12805] 12985 13083 13181 13279 13377 13475 13573 13671 13769 13867 13965 14063
## [12817] 14161 14259 14357 14455 14553 14651 14749 14847 14945 15043 15141 15239
## [12829] 15337 15435 15533 15631 15729 15827 15925 16023 16121 16219 16317 16415
## [12841] 16513 16611 16709 16807 16905 17003 17101 17199 17297 17395 17493 17591
## [12853] 17689 17787 17885 17983 18081 18179 18277 18375 18473 18571 18669 18767
## [12865] 18865 18963 19061 19159 19257 19355 19453 19551 19649 19747 19845 19943
## [12877] 20041 20139 20237 20335 20433 20531 20629 20727 20825 20923 21021 21119
## [12889] 21217 21315 21413 21511 21609 21707 21805 21903 22001 22099 22197 22295
## [12901] 22393 22491 22589 22687 22785 22883 22981 23079 23177 23275 23373 23471
## [12913] 23569 23667 23765 23863 23961 24059 24157 24255 24353 24451 24549 24647
## [12925] 24745 24843 24941 25039 25137 25235 25333 25431 25529 25627 25725 25823
## [12937]    50   148   246   344   442   540   638   736   834   932  1030  1128
## [12949]  1226  1324  1422  1520  1618  1716  1814  1912  2010  2108  2206  2304
## [12961]  2402  2500  2598  2696  2794  2892  2990  3088  3186  3284  3382  3480
## [12973]  3578  3676  3774  3872  3970  4068  4166  4264  4362  4460  4558  4656
## [12985]  4754  4852  4950  5048  5146  5244  5342  5440  5538  5636  5734  5832
## [12997]  5930  6028  6126  6224  6322  6420  6518  6616  6714  6812  6910  7008
## [13009]  7106  7204  7302  7400  7498  7596  7694  7792  7890  7988  8086  8184
## [13021]  8282  8380  8478  8576  8674  8772  8870  8968  9066  9164  9262  9360
## [13033]  9458  9556  9654  9752  9850  9948 10046 10144 10242 10340 10438 10536
## [13045] 10634 10732 10830 10928 11026 11124 11222 11320 11418 11516 11614 11712
## [13057] 11810 11908 12006 12104 12202 12300 12398 12496 12594 12692 12790 12888
## [13069] 12986 13084 13182 13280 13378 13476 13574 13672 13770 13868 13966 14064
## [13081] 14162 14260 14358 14456 14554 14652 14750 14848 14946 15044 15142 15240
## [13093] 15338 15436 15534 15632 15730 15828 15926 16024 16122 16220 16318 16416
## [13105] 16514 16612 16710 16808 16906 17004 17102 17200 17298 17396 17494 17592
## [13117] 17690 17788 17886 17984 18082 18180 18278 18376 18474 18572 18670 18768
## [13129] 18866 18964 19062 19160 19258 19356 19454 19552 19650 19748 19846 19944
## [13141] 20042 20140 20238 20336 20434 20532 20630 20728 20826 20924 21022 21120
## [13153] 21218 21316 21414 21512 21610 21708 21806 21904 22002 22100 22198 22296
## [13165] 22394 22492 22590 22688 22786 22884 22982 23080 23178 23276 23374 23472
## [13177] 23570 23668 23766 23864 23962 24060 24158 24256 24354 24452 24550 24648
## [13189] 24746 24844 24942 25040 25138 25236 25334 25432 25530 25628 25726 25824
## [13201]    51   149   247   345   443   541   639   737   835   933  1031  1129
## [13213]  1227  1325  1423  1521  1619  1717  1815  1913  2011  2109  2207  2305
## [13225]  2403  2501  2599  2697  2795  2893  2991  3089  3187  3285  3383  3481
## [13237]  3579  3677  3775  3873  3971  4069  4167  4265  4363  4461  4559  4657
## [13249]  4755  4853  4951  5049  5147  5245  5343  5441  5539  5637  5735  5833
## [13261]  5931  6029  6127  6225  6323  6421  6519  6617  6715  6813  6911  7009
## [13273]  7107  7205  7303  7401  7499  7597  7695  7793  7891  7989  8087  8185
## [13285]  8283  8381  8479  8577  8675  8773  8871  8969  9067  9165  9263  9361
## [13297]  9459  9557  9655  9753  9851  9949 10047 10145 10243 10341 10439 10537
## [13309] 10635 10733 10831 10929 11027 11125 11223 11321 11419 11517 11615 11713
## [13321] 11811 11909 12007 12105 12203 12301 12399 12497 12595 12693 12791 12889
## [13333] 12987 13085 13183 13281 13379 13477 13575 13673 13771 13869 13967 14065
## [13345] 14163 14261 14359 14457 14555 14653 14751 14849 14947 15045 15143 15241
## [13357] 15339 15437 15535 15633 15731 15829 15927 16025 16123 16221 16319 16417
## [13369] 16515 16613 16711 16809 16907 17005 17103 17201 17299 17397 17495 17593
## [13381] 17691 17789 17887 17985 18083 18181 18279 18377 18475 18573 18671 18769
## [13393] 18867 18965 19063 19161 19259 19357 19455 19553 19651 19749 19847 19945
## [13405] 20043 20141 20239 20337 20435 20533 20631 20729 20827 20925 21023 21121
## [13417] 21219 21317 21415 21513 21611 21709 21807 21905 22003 22101 22199 22297
## [13429] 22395 22493 22591 22689 22787 22885 22983 23081 23179 23277 23375 23473
## [13441] 23571 23669 23767 23865 23963 24061 24159 24257 24355 24453 24551 24649
## [13453] 24747 24845 24943 25041 25139 25237 25335 25433 25531 25629 25727 25825
## [13465]    52   150   248   346   444   542   640   738   836   934  1032  1130
## [13477]  1228  1326  1424  1522  1620  1718  1816  1914  2012  2110  2208  2306
## [13489]  2404  2502  2600  2698  2796  2894  2992  3090  3188  3286  3384  3482
## [13501]  3580  3678  3776  3874  3972  4070  4168  4266  4364  4462  4560  4658
## [13513]  4756  4854  4952  5050  5148  5246  5344  5442  5540  5638  5736  5834
## [13525]  5932  6030  6128  6226  6324  6422  6520  6618  6716  6814  6912  7010
## [13537]  7108  7206  7304  7402  7500  7598  7696  7794  7892  7990  8088  8186
## [13549]  8284  8382  8480  8578  8676  8774  8872  8970  9068  9166  9264  9362
## [13561]  9460  9558  9656  9754  9852  9950 10048 10146 10244 10342 10440 10538
## [13573] 10636 10734 10832 10930 11028 11126 11224 11322 11420 11518 11616 11714
## [13585] 11812 11910 12008 12106 12204 12302 12400 12498 12596 12694 12792 12890
## [13597] 12988 13086 13184 13282 13380 13478 13576 13674 13772 13870 13968 14066
## [13609] 14164 14262 14360 14458 14556 14654 14752 14850 14948 15046 15144 15242
## [13621] 15340 15438 15536 15634 15732 15830 15928 16026 16124 16222 16320 16418
## [13633] 16516 16614 16712 16810 16908 17006 17104 17202 17300 17398 17496 17594
## [13645] 17692 17790 17888 17986 18084 18182 18280 18378 18476 18574 18672 18770
## [13657] 18868 18966 19064 19162 19260 19358 19456 19554 19652 19750 19848 19946
## [13669] 20044 20142 20240 20338 20436 20534 20632 20730 20828 20926 21024 21122
## [13681] 21220 21318 21416 21514 21612 21710 21808 21906 22004 22102 22200 22298
## [13693] 22396 22494 22592 22690 22788 22886 22984 23082 23180 23278 23376 23474
## [13705] 23572 23670 23768 23866 23964 24062 24160 24258 24356 24454 24552 24650
## [13717] 24748 24846 24944 25042 25140 25238 25336 25434 25532 25630 25728 25826
## [13729]    53   151   249   347   445   543   641   739   837   935  1033  1131
## [13741]  1229  1327  1425  1523  1621  1719  1817  1915  2013  2111  2209  2307
## [13753]  2405  2503  2601  2699  2797  2895  2993  3091  3189  3287  3385  3483
## [13765]  3581  3679  3777  3875  3973  4071  4169  4267  4365  4463  4561  4659
## [13777]  4757  4855  4953  5051  5149  5247  5345  5443  5541  5639  5737  5835
## [13789]  5933  6031  6129  6227  6325  6423  6521  6619  6717  6815  6913  7011
## [13801]  7109  7207  7305  7403  7501  7599  7697  7795  7893  7991  8089  8187
## [13813]  8285  8383  8481  8579  8677  8775  8873  8971  9069  9167  9265  9363
## [13825]  9461  9559  9657  9755  9853  9951 10049 10147 10245 10343 10441 10539
## [13837] 10637 10735 10833 10931 11029 11127 11225 11323 11421 11519 11617 11715
## [13849] 11813 11911 12009 12107 12205 12303 12401 12499 12597 12695 12793 12891
## [13861] 12989 13087 13185 13283 13381 13479 13577 13675 13773 13871 13969 14067
## [13873] 14165 14263 14361 14459 14557 14655 14753 14851 14949 15047 15145 15243
## [13885] 15341 15439 15537 15635 15733 15831 15929 16027 16125 16223 16321 16419
## [13897] 16517 16615 16713 16811 16909 17007 17105 17203 17301 17399 17497 17595
## [13909] 17693 17791 17889 17987 18085 18183 18281 18379 18477 18575 18673 18771
## [13921] 18869 18967 19065 19163 19261 19359 19457 19555 19653 19751 19849 19947
## [13933] 20045 20143 20241 20339 20437 20535 20633 20731 20829 20927 21025 21123
## [13945] 21221 21319 21417 21515 21613 21711 21809 21907 22005 22103 22201 22299
## [13957] 22397 22495 22593 22691 22789 22887 22985 23083 23181 23279 23377 23475
## [13969] 23573 23671 23769 23867 23965 24063 24161 24259 24357 24455 24553 24651
## [13981] 24749 24847 24945 25043 25141 25239 25337 25435 25533 25631 25729 25827
## [13993]    54   152   250   348   446   544   642   740   838   936  1034  1132
## [14005]  1230  1328  1426  1524  1622  1720  1818  1916  2014  2112  2210  2308
## [14017]  2406  2504  2602  2700  2798  2896  2994  3092  3190  3288  3386  3484
## [14029]  3582  3680  3778  3876  3974  4072  4170  4268  4366  4464  4562  4660
## [14041]  4758  4856  4954  5052  5150  5248  5346  5444  5542  5640  5738  5836
## [14053]  5934  6032  6130  6228  6326  6424  6522  6620  6718  6816  6914  7012
## [14065]  7110  7208  7306  7404  7502  7600  7698  7796  7894  7992  8090  8188
## [14077]  8286  8384  8482  8580  8678  8776  8874  8972  9070  9168  9266  9364
## [14089]  9462  9560  9658  9756  9854  9952 10050 10148 10246 10344 10442 10540
## [14101] 10638 10736 10834 10932 11030 11128 11226 11324 11422 11520 11618 11716
## [14113] 11814 11912 12010 12108 12206 12304 12402 12500 12598 12696 12794 12892
## [14125] 12990 13088 13186 13284 13382 13480 13578 13676 13774 13872 13970 14068
## [14137] 14166 14264 14362 14460 14558 14656 14754 14852 14950 15048 15146 15244
## [14149] 15342 15440 15538 15636 15734 15832 15930 16028 16126 16224 16322 16420
## [14161] 16518 16616 16714 16812 16910 17008 17106 17204 17302 17400 17498 17596
## [14173] 17694 17792 17890 17988 18086 18184 18282 18380 18478 18576 18674 18772
## [14185] 18870 18968 19066 19164 19262 19360 19458 19556 19654 19752 19850 19948
## [14197] 20046 20144 20242 20340 20438 20536 20634 20732 20830 20928 21026 21124
## [14209] 21222 21320 21418 21516 21614 21712 21810 21908 22006 22104 22202 22300
## [14221] 22398 22496 22594 22692 22790 22888 22986 23084 23182 23280 23378 23476
## [14233] 23574 23672 23770 23868 23966 24064 24162 24260 24358 24456 24554 24652
## [14245] 24750 24848 24946 25044 25142 25240 25338 25436 25534 25632 25730 25828
## [14257]    55   153   251   349   447   545   643   741   839   937  1035  1133
## [14269]  1231  1329  1427  1525  1623  1721  1819  1917  2015  2113  2211  2309
## [14281]  2407  2505  2603  2701  2799  2897  2995  3093  3191  3289  3387  3485
## [14293]  3583  3681  3779  3877  3975  4073  4171  4269  4367  4465  4563  4661
## [14305]  4759  4857  4955  5053  5151  5249  5347  5445  5543  5641  5739  5837
## [14317]  5935  6033  6131  6229  6327  6425  6523  6621  6719  6817  6915  7013
## [14329]  7111  7209  7307  7405  7503  7601  7699  7797  7895  7993  8091  8189
## [14341]  8287  8385  8483  8581  8679  8777  8875  8973  9071  9169  9267  9365
## [14353]  9463  9561  9659  9757  9855  9953 10051 10149 10247 10345 10443 10541
## [14365] 10639 10737 10835 10933 11031 11129 11227 11325 11423 11521 11619 11717
## [14377] 11815 11913 12011 12109 12207 12305 12403 12501 12599 12697 12795 12893
## [14389] 12991 13089 13187 13285 13383 13481 13579 13677 13775 13873 13971 14069
## [14401] 14167 14265 14363 14461 14559 14657 14755 14853 14951 15049 15147 15245
## [14413] 15343 15441 15539 15637 15735 15833 15931 16029 16127 16225 16323 16421
## [14425] 16519 16617 16715 16813 16911 17009 17107 17205 17303 17401 17499 17597
## [14437] 17695 17793 17891 17989 18087 18185 18283 18381 18479 18577 18675 18773
## [14449] 18871 18969 19067 19165 19263 19361 19459 19557 19655 19753 19851 19949
## [14461] 20047 20145 20243 20341 20439 20537 20635 20733 20831 20929 21027 21125
## [14473] 21223 21321 21419 21517 21615 21713 21811 21909 22007 22105 22203 22301
## [14485] 22399 22497 22595 22693 22791 22889 22987 23085 23183 23281 23379 23477
## [14497] 23575 23673 23771 23869 23967 24065 24163 24261 24359 24457 24555 24653
## [14509] 24751 24849 24947 25045 25143 25241 25339 25437 25535 25633 25731 25829
## [14521]    56   154   252   350   448   546   644   742   840   938  1036  1134
## [14533]  1232  1330  1428  1526  1624  1722  1820  1918  2016  2114  2212  2310
## [14545]  2408  2506  2604  2702  2800  2898  2996  3094  3192  3290  3388  3486
## [14557]  3584  3682  3780  3878  3976  4074  4172  4270  4368  4466  4564  4662
## [14569]  4760  4858  4956  5054  5152  5250  5348  5446  5544  5642  5740  5838
## [14581]  5936  6034  6132  6230  6328  6426  6524  6622  6720  6818  6916  7014
## [14593]  7112  7210  7308  7406  7504  7602  7700  7798  7896  7994  8092  8190
## [14605]  8288  8386  8484  8582  8680  8778  8876  8974  9072  9170  9268  9366
## [14617]  9464  9562  9660  9758  9856  9954 10052 10150 10248 10346 10444 10542
## [14629] 10640 10738 10836 10934 11032 11130 11228 11326 11424 11522 11620 11718
## [14641] 11816 11914 12012 12110 12208 12306 12404 12502 12600 12698 12796 12894
## [14653] 12992 13090 13188 13286 13384 13482 13580 13678 13776 13874 13972 14070
## [14665] 14168 14266 14364 14462 14560 14658 14756 14854 14952 15050 15148 15246
## [14677] 15344 15442 15540 15638 15736 15834 15932 16030 16128 16226 16324 16422
## [14689] 16520 16618 16716 16814 16912 17010 17108 17206 17304 17402 17500 17598
## [14701] 17696 17794 17892 17990 18088 18186 18284 18382 18480 18578 18676 18774
## [14713] 18872 18970 19068 19166 19264 19362 19460 19558 19656 19754 19852 19950
## [14725] 20048 20146 20244 20342 20440 20538 20636 20734 20832 20930 21028 21126
## [14737] 21224 21322 21420 21518 21616 21714 21812 21910 22008 22106 22204 22302
## [14749] 22400 22498 22596 22694 22792 22890 22988 23086 23184 23282 23380 23478
## [14761] 23576 23674 23772 23870 23968 24066 24164 24262 24360 24458 24556 24654
## [14773] 24752 24850 24948 25046 25144 25242 25340 25438 25536 25634 25732 25830
## [14785]    57   155   253   351   449   547   645   743   841   939  1037  1135
## [14797]  1233  1331  1429  1527  1625  1723  1821  1919  2017  2115  2213  2311
## [14809]  2409  2507  2605  2703  2801  2899  2997  3095  3193  3291  3389  3487
## [14821]  3585  3683  3781  3879  3977  4075  4173  4271  4369  4467  4565  4663
## [14833]  4761  4859  4957  5055  5153  5251  5349  5447  5545  5643  5741  5839
## [14845]  5937  6035  6133  6231  6329  6427  6525  6623  6721  6819  6917  7015
## [14857]  7113  7211  7309  7407  7505  7603  7701  7799  7897  7995  8093  8191
## [14869]  8289  8387  8485  8583  8681  8779  8877  8975  9073  9171  9269  9367
## [14881]  9465  9563  9661  9759  9857  9955 10053 10151 10249 10347 10445 10543
## [14893] 10641 10739 10837 10935 11033 11131 11229 11327 11425 11523 11621 11719
## [14905] 11817 11915 12013 12111 12209 12307 12405 12503 12601 12699 12797 12895
## [14917] 12993 13091 13189 13287 13385 13483 13581 13679 13777 13875 13973 14071
## [14929] 14169 14267 14365 14463 14561 14659 14757 14855 14953 15051 15149 15247
## [14941] 15345 15443 15541 15639 15737 15835 15933 16031 16129 16227 16325 16423
## [14953] 16521 16619 16717 16815 16913 17011 17109 17207 17305 17403 17501 17599
## [14965] 17697 17795 17893 17991 18089 18187 18285 18383 18481 18579 18677 18775
## [14977] 18873 18971 19069 19167 19265 19363 19461 19559 19657 19755 19853 19951
## [14989] 20049 20147 20245 20343 20441 20539 20637 20735 20833 20931 21029 21127
## [15001] 21225 21323 21421 21519 21617 21715 21813 21911 22009 22107 22205 22303
## [15013] 22401 22499 22597 22695 22793 22891 22989 23087 23185 23283 23381 23479
## [15025] 23577 23675 23773 23871 23969 24067 24165 24263 24361 24459 24557 24655
## [15037] 24753 24851 24949 25047 25145 25243 25341 25439 25537 25635 25733 25831
## [15049]    58   156   254   352   450   548   646   744   842   940  1038  1136
## [15061]  1234  1332  1430  1528  1626  1724  1822  1920  2018  2116  2214  2312
## [15073]  2410  2508  2606  2704  2802  2900  2998  3096  3194  3292  3390  3488
## [15085]  3586  3684  3782  3880  3978  4076  4174  4272  4370  4468  4566  4664
## [15097]  4762  4860  4958  5056  5154  5252  5350  5448  5546  5644  5742  5840
## [15109]  5938  6036  6134  6232  6330  6428  6526  6624  6722  6820  6918  7016
## [15121]  7114  7212  7310  7408  7506  7604  7702  7800  7898  7996  8094  8192
## [15133]  8290  8388  8486  8584  8682  8780  8878  8976  9074  9172  9270  9368
## [15145]  9466  9564  9662  9760  9858  9956 10054 10152 10250 10348 10446 10544
## [15157] 10642 10740 10838 10936 11034 11132 11230 11328 11426 11524 11622 11720
## [15169] 11818 11916 12014 12112 12210 12308 12406 12504 12602 12700 12798 12896
## [15181] 12994 13092 13190 13288 13386 13484 13582 13680 13778 13876 13974 14072
## [15193] 14170 14268 14366 14464 14562 14660 14758 14856 14954 15052 15150 15248
## [15205] 15346 15444 15542 15640 15738 15836 15934 16032 16130 16228 16326 16424
## [15217] 16522 16620 16718 16816 16914 17012 17110 17208 17306 17404 17502 17600
## [15229] 17698 17796 17894 17992 18090 18188 18286 18384 18482 18580 18678 18776
## [15241] 18874 18972 19070 19168 19266 19364 19462 19560 19658 19756 19854 19952
## [15253] 20050 20148 20246 20344 20442 20540 20638 20736 20834 20932 21030 21128
## [15265] 21226 21324 21422 21520 21618 21716 21814 21912 22010 22108 22206 22304
## [15277] 22402 22500 22598 22696 22794 22892 22990 23088 23186 23284 23382 23480
## [15289] 23578 23676 23774 23872 23970 24068 24166 24264 24362 24460 24558 24656
## [15301] 24754 24852 24950 25048 25146 25244 25342 25440 25538 25636 25734 25832
## [15313]    59   157   255   353   451   549   647   745   843   941  1039  1137
## [15325]  1235  1333  1431  1529  1627  1725  1823  1921  2019  2117  2215  2313
## [15337]  2411  2509  2607  2705  2803  2901  2999  3097  3195  3293  3391  3489
## [15349]  3587  3685  3783  3881  3979  4077  4175  4273  4371  4469  4567  4665
## [15361]  4763  4861  4959  5057  5155  5253  5351  5449  5547  5645  5743  5841
## [15373]  5939  6037  6135  6233  6331  6429  6527  6625  6723  6821  6919  7017
## [15385]  7115  7213  7311  7409  7507  7605  7703  7801  7899  7997  8095  8193
## [15397]  8291  8389  8487  8585  8683  8781  8879  8977  9075  9173  9271  9369
## [15409]  9467  9565  9663  9761  9859  9957 10055 10153 10251 10349 10447 10545
## [15421] 10643 10741 10839 10937 11035 11133 11231 11329 11427 11525 11623 11721
## [15433] 11819 11917 12015 12113 12211 12309 12407 12505 12603 12701 12799 12897
## [15445] 12995 13093 13191 13289 13387 13485 13583 13681 13779 13877 13975 14073
## [15457] 14171 14269 14367 14465 14563 14661 14759 14857 14955 15053 15151 15249
## [15469] 15347 15445 15543 15641 15739 15837 15935 16033 16131 16229 16327 16425
## [15481] 16523 16621 16719 16817 16915 17013 17111 17209 17307 17405 17503 17601
## [15493] 17699 17797 17895 17993 18091 18189 18287 18385 18483 18581 18679 18777
## [15505] 18875 18973 19071 19169 19267 19365 19463 19561 19659 19757 19855 19953
## [15517] 20051 20149 20247 20345 20443 20541 20639 20737 20835 20933 21031 21129
## [15529] 21227 21325 21423 21521 21619 21717 21815 21913 22011 22109 22207 22305
## [15541] 22403 22501 22599 22697 22795 22893 22991 23089 23187 23285 23383 23481
## [15553] 23579 23677 23775 23873 23971 24069 24167 24265 24363 24461 24559 24657
## [15565] 24755 24853 24951 25049 25147 25245 25343 25441 25539 25637 25735 25833
## [15577]    60   158   256   354   452   550   648   746   844   942  1040  1138
## [15589]  1236  1334  1432  1530  1628  1726  1824  1922  2020  2118  2216  2314
## [15601]  2412  2510  2608  2706  2804  2902  3000  3098  3196  3294  3392  3490
## [15613]  3588  3686  3784  3882  3980  4078  4176  4274  4372  4470  4568  4666
## [15625]  4764  4862  4960  5058  5156  5254  5352  5450  5548  5646  5744  5842
## [15637]  5940  6038  6136  6234  6332  6430  6528  6626  6724  6822  6920  7018
## [15649]  7116  7214  7312  7410  7508  7606  7704  7802  7900  7998  8096  8194
## [15661]  8292  8390  8488  8586  8684  8782  8880  8978  9076  9174  9272  9370
## [15673]  9468  9566  9664  9762  9860  9958 10056 10154 10252 10350 10448 10546
## [15685] 10644 10742 10840 10938 11036 11134 11232 11330 11428 11526 11624 11722
## [15697] 11820 11918 12016 12114 12212 12310 12408 12506 12604 12702 12800 12898
## [15709] 12996 13094 13192 13290 13388 13486 13584 13682 13780 13878 13976 14074
## [15721] 14172 14270 14368 14466 14564 14662 14760 14858 14956 15054 15152 15250
## [15733] 15348 15446 15544 15642 15740 15838 15936 16034 16132 16230 16328 16426
## [15745] 16524 16622 16720 16818 16916 17014 17112 17210 17308 17406 17504 17602
## [15757] 17700 17798 17896 17994 18092 18190 18288 18386 18484 18582 18680 18778
## [15769] 18876 18974 19072 19170 19268 19366 19464 19562 19660 19758 19856 19954
## [15781] 20052 20150 20248 20346 20444 20542 20640 20738 20836 20934 21032 21130
## [15793] 21228 21326 21424 21522 21620 21718 21816 21914 22012 22110 22208 22306
## [15805] 22404 22502 22600 22698 22796 22894 22992 23090 23188 23286 23384 23482
## [15817] 23580 23678 23776 23874 23972 24070 24168 24266 24364 24462 24560 24658
## [15829] 24756 24854 24952 25050 25148 25246 25344 25442 25540 25638 25736 25834
## [15841]    61   159   257   355   453   551   649   747   845   943  1041  1139
## [15853]  1237  1335  1433  1531  1629  1727  1825  1923  2021  2119  2217  2315
## [15865]  2413  2511  2609  2707  2805  2903  3001  3099  3197  3295  3393  3491
## [15877]  3589  3687  3785  3883  3981  4079  4177  4275  4373  4471  4569  4667
## [15889]  4765  4863  4961  5059  5157  5255  5353  5451  5549  5647  5745  5843
## [15901]  5941  6039  6137  6235  6333  6431  6529  6627  6725  6823  6921  7019
## [15913]  7117  7215  7313  7411  7509  7607  7705  7803  7901  7999  8097  8195
## [15925]  8293  8391  8489  8587  8685  8783  8881  8979  9077  9175  9273  9371
## [15937]  9469  9567  9665  9763  9861  9959 10057 10155 10253 10351 10449 10547
## [15949] 10645 10743 10841 10939 11037 11135 11233 11331 11429 11527 11625 11723
## [15961] 11821 11919 12017 12115 12213 12311 12409 12507 12605 12703 12801 12899
## [15973] 12997 13095 13193 13291 13389 13487 13585 13683 13781 13879 13977 14075
## [15985] 14173 14271 14369 14467 14565 14663 14761 14859 14957 15055 15153 15251
## [15997] 15349 15447 15545 15643 15741 15839 15937 16035 16133 16231 16329 16427
## [16009] 16525 16623 16721 16819 16917 17015 17113 17211 17309 17407 17505 17603
## [16021] 17701 17799 17897 17995 18093 18191 18289 18387 18485 18583 18681 18779
## [16033] 18877 18975 19073 19171 19269 19367 19465 19563 19661 19759 19857 19955
## [16045] 20053 20151 20249 20347 20445 20543 20641 20739 20837 20935 21033 21131
## [16057] 21229 21327 21425 21523 21621 21719 21817 21915 22013 22111 22209 22307
## [16069] 22405 22503 22601 22699 22797 22895 22993 23091 23189 23287 23385 23483
## [16081] 23581 23679 23777 23875 23973 24071 24169 24267 24365 24463 24561 24659
## [16093] 24757 24855 24953 25051 25149 25247 25345 25443 25541 25639 25737 25835
## [16105]    62   160   258   356   454   552   650   748   846   944  1042  1140
## [16117]  1238  1336  1434  1532  1630  1728  1826  1924  2022  2120  2218  2316
## [16129]  2414  2512  2610  2708  2806  2904  3002  3100  3198  3296  3394  3492
## [16141]  3590  3688  3786  3884  3982  4080  4178  4276  4374  4472  4570  4668
## [16153]  4766  4864  4962  5060  5158  5256  5354  5452  5550  5648  5746  5844
## [16165]  5942  6040  6138  6236  6334  6432  6530  6628  6726  6824  6922  7020
## [16177]  7118  7216  7314  7412  7510  7608  7706  7804  7902  8000  8098  8196
## [16189]  8294  8392  8490  8588  8686  8784  8882  8980  9078  9176  9274  9372
## [16201]  9470  9568  9666  9764  9862  9960 10058 10156 10254 10352 10450 10548
## [16213] 10646 10744 10842 10940 11038 11136 11234 11332 11430 11528 11626 11724
## [16225] 11822 11920 12018 12116 12214 12312 12410 12508 12606 12704 12802 12900
## [16237] 12998 13096 13194 13292 13390 13488 13586 13684 13782 13880 13978 14076
## [16249] 14174 14272 14370 14468 14566 14664 14762 14860 14958 15056 15154 15252
## [16261] 15350 15448 15546 15644 15742 15840 15938 16036 16134 16232 16330 16428
## [16273] 16526 16624 16722 16820 16918 17016 17114 17212 17310 17408 17506 17604
## [16285] 17702 17800 17898 17996 18094 18192 18290 18388 18486 18584 18682 18780
## [16297] 18878 18976 19074 19172 19270 19368 19466 19564 19662 19760 19858 19956
## [16309] 20054 20152 20250 20348 20446 20544 20642 20740 20838 20936 21034 21132
## [16321] 21230 21328 21426 21524 21622 21720 21818 21916 22014 22112 22210 22308
## [16333] 22406 22504 22602 22700 22798 22896 22994 23092 23190 23288 23386 23484
## [16345] 23582 23680 23778 23876 23974 24072 24170 24268 24366 24464 24562 24660
## [16357] 24758 24856 24954 25052 25150 25248 25346 25444 25542 25640 25738 25836
## [16369]    63   161   259   357   455   553   651   749   847   945  1043  1141
## [16381]  1239  1337  1435  1533  1631  1729  1827  1925  2023  2121  2219  2317
## [16393]  2415  2513  2611  2709  2807  2905  3003  3101  3199  3297  3395  3493
## [16405]  3591  3689  3787  3885  3983  4081  4179  4277  4375  4473  4571  4669
## [16417]  4767  4865  4963  5061  5159  5257  5355  5453  5551  5649  5747  5845
## [16429]  5943  6041  6139  6237  6335  6433  6531  6629  6727  6825  6923  7021
## [16441]  7119  7217  7315  7413  7511  7609  7707  7805  7903  8001  8099  8197
## [16453]  8295  8393  8491  8589  8687  8785  8883  8981  9079  9177  9275  9373
## [16465]  9471  9569  9667  9765  9863  9961 10059 10157 10255 10353 10451 10549
## [16477] 10647 10745 10843 10941 11039 11137 11235 11333 11431 11529 11627 11725
## [16489] 11823 11921 12019 12117 12215 12313 12411 12509 12607 12705 12803 12901
## [16501] 12999 13097 13195 13293 13391 13489 13587 13685 13783 13881 13979 14077
## [16513] 14175 14273 14371 14469 14567 14665 14763 14861 14959 15057 15155 15253
## [16525] 15351 15449 15547 15645 15743 15841 15939 16037 16135 16233 16331 16429
## [16537] 16527 16625 16723 16821 16919 17017 17115 17213 17311 17409 17507 17605
## [16549] 17703 17801 17899 17997 18095 18193 18291 18389 18487 18585 18683 18781
## [16561] 18879 18977 19075 19173 19271 19369 19467 19565 19663 19761 19859 19957
## [16573] 20055 20153 20251 20349 20447 20545 20643 20741 20839 20937 21035 21133
## [16585] 21231 21329 21427 21525 21623 21721 21819 21917 22015 22113 22211 22309
## [16597] 22407 22505 22603 22701 22799 22897 22995 23093 23191 23289 23387 23485
## [16609] 23583 23681 23779 23877 23975 24073 24171 24269 24367 24465 24563 24661
## [16621] 24759 24857 24955 25053 25151 25249 25347 25445 25543 25641 25739 25837
## [16633]    64   162   260   358   456   554   652   750   848   946  1044  1142
## [16645]  1240  1338  1436  1534  1632  1730  1828  1926  2024  2122  2220  2318
## [16657]  2416  2514  2612  2710  2808  2906  3004  3102  3200  3298  3396  3494
## [16669]  3592  3690  3788  3886  3984  4082  4180  4278  4376  4474  4572  4670
## [16681]  4768  4866  4964  5062  5160  5258  5356  5454  5552  5650  5748  5846
## [16693]  5944  6042  6140  6238  6336  6434  6532  6630  6728  6826  6924  7022
## [16705]  7120  7218  7316  7414  7512  7610  7708  7806  7904  8002  8100  8198
## [16717]  8296  8394  8492  8590  8688  8786  8884  8982  9080  9178  9276  9374
## [16729]  9472  9570  9668  9766  9864  9962 10060 10158 10256 10354 10452 10550
## [16741] 10648 10746 10844 10942 11040 11138 11236 11334 11432 11530 11628 11726
## [16753] 11824 11922 12020 12118 12216 12314 12412 12510 12608 12706 12804 12902
## [16765] 13000 13098 13196 13294 13392 13490 13588 13686 13784 13882 13980 14078
## [16777] 14176 14274 14372 14470 14568 14666 14764 14862 14960 15058 15156 15254
## [16789] 15352 15450 15548 15646 15744 15842 15940 16038 16136 16234 16332 16430
## [16801] 16528 16626 16724 16822 16920 17018 17116 17214 17312 17410 17508 17606
## [16813] 17704 17802 17900 17998 18096 18194 18292 18390 18488 18586 18684 18782
## [16825] 18880 18978 19076 19174 19272 19370 19468 19566 19664 19762 19860 19958
## [16837] 20056 20154 20252 20350 20448 20546 20644 20742 20840 20938 21036 21134
## [16849] 21232 21330 21428 21526 21624 21722 21820 21918 22016 22114 22212 22310
## [16861] 22408 22506 22604 22702 22800 22898 22996 23094 23192 23290 23388 23486
## [16873] 23584 23682 23780 23878 23976 24074 24172 24270 24368 24466 24564 24662
## [16885] 24760 24858 24956 25054 25152 25250 25348 25446 25544 25642 25740 25838
## [16897]    65   163   261   359   457   555   653   751   849   947  1045  1143
## [16909]  1241  1339  1437  1535  1633  1731  1829  1927  2025  2123  2221  2319
## [16921]  2417  2515  2613  2711  2809  2907  3005  3103  3201  3299  3397  3495
## [16933]  3593  3691  3789  3887  3985  4083  4181  4279  4377  4475  4573  4671
## [16945]  4769  4867  4965  5063  5161  5259  5357  5455  5553  5651  5749  5847
## [16957]  5945  6043  6141  6239  6337  6435  6533  6631  6729  6827  6925  7023
## [16969]  7121  7219  7317  7415  7513  7611  7709  7807  7905  8003  8101  8199
## [16981]  8297  8395  8493  8591  8689  8787  8885  8983  9081  9179  9277  9375
## [16993]  9473  9571  9669  9767  9865  9963 10061 10159 10257 10355 10453 10551
## [17005] 10649 10747 10845 10943 11041 11139 11237 11335 11433 11531 11629 11727
## [17017] 11825 11923 12021 12119 12217 12315 12413 12511 12609 12707 12805 12903
## [17029] 13001 13099 13197 13295 13393 13491 13589 13687 13785 13883 13981 14079
## [17041] 14177 14275 14373 14471 14569 14667 14765 14863 14961 15059 15157 15255
## [17053] 15353 15451 15549 15647 15745 15843 15941 16039 16137 16235 16333 16431
## [17065] 16529 16627 16725 16823 16921 17019 17117 17215 17313 17411 17509 17607
## [17077] 17705 17803 17901 17999 18097 18195 18293 18391 18489 18587 18685 18783
## [17089] 18881 18979 19077 19175 19273 19371 19469 19567 19665 19763 19861 19959
## [17101] 20057 20155 20253 20351 20449 20547 20645 20743 20841 20939 21037 21135
## [17113] 21233 21331 21429 21527 21625 21723 21821 21919 22017 22115 22213 22311
## [17125] 22409 22507 22605 22703 22801 22899 22997 23095 23193 23291 23389 23487
## [17137] 23585 23683 23781 23879 23977 24075 24173 24271 24369 24467 24565 24663
## [17149] 24761 24859 24957 25055 25153 25251 25349 25447 25545 25643 25741 25839
## [17161]    66   164   262   360   458   556   654   752   850   948  1046  1144
## [17173]  1242  1340  1438  1536  1634  1732  1830  1928  2026  2124  2222  2320
## [17185]  2418  2516  2614  2712  2810  2908  3006  3104  3202  3300  3398  3496
## [17197]  3594  3692  3790  3888  3986  4084  4182  4280  4378  4476  4574  4672
## [17209]  4770  4868  4966  5064  5162  5260  5358  5456  5554  5652  5750  5848
## [17221]  5946  6044  6142  6240  6338  6436  6534  6632  6730  6828  6926  7024
## [17233]  7122  7220  7318  7416  7514  7612  7710  7808  7906  8004  8102  8200
## [17245]  8298  8396  8494  8592  8690  8788  8886  8984  9082  9180  9278  9376
## [17257]  9474  9572  9670  9768  9866  9964 10062 10160 10258 10356 10454 10552
## [17269] 10650 10748 10846 10944 11042 11140 11238 11336 11434 11532 11630 11728
## [17281] 11826 11924 12022 12120 12218 12316 12414 12512 12610 12708 12806 12904
## [17293] 13002 13100 13198 13296 13394 13492 13590 13688 13786 13884 13982 14080
## [17305] 14178 14276 14374 14472 14570 14668 14766 14864 14962 15060 15158 15256
## [17317] 15354 15452 15550 15648 15746 15844 15942 16040 16138 16236 16334 16432
## [17329] 16530 16628 16726 16824 16922 17020 17118 17216 17314 17412 17510 17608
## [17341] 17706 17804 17902 18000 18098 18196 18294 18392 18490 18588 18686 18784
## [17353] 18882 18980 19078 19176 19274 19372 19470 19568 19666 19764 19862 19960
## [17365] 20058 20156 20254 20352 20450 20548 20646 20744 20842 20940 21038 21136
## [17377] 21234 21332 21430 21528 21626 21724 21822 21920 22018 22116 22214 22312
## [17389] 22410 22508 22606 22704 22802 22900 22998 23096 23194 23292 23390 23488
## [17401] 23586 23684 23782 23880 23978 24076 24174 24272 24370 24468 24566 24664
## [17413] 24762 24860 24958 25056 25154 25252 25350 25448 25546 25644 25742 25840
## [17425]    67   165   263   361   459   557   655   753   851   949  1047  1145
## [17437]  1243  1341  1439  1537  1635  1733  1831  1929  2027  2125  2223  2321
## [17449]  2419  2517  2615  2713  2811  2909  3007  3105  3203  3301  3399  3497
## [17461]  3595  3693  3791  3889  3987  4085  4183  4281  4379  4477  4575  4673
## [17473]  4771  4869  4967  5065  5163  5261  5359  5457  5555  5653  5751  5849
## [17485]  5947  6045  6143  6241  6339  6437  6535  6633  6731  6829  6927  7025
## [17497]  7123  7221  7319  7417  7515  7613  7711  7809  7907  8005  8103  8201
## [17509]  8299  8397  8495  8593  8691  8789  8887  8985  9083  9181  9279  9377
## [17521]  9475  9573  9671  9769  9867  9965 10063 10161 10259 10357 10455 10553
## [17533] 10651 10749 10847 10945 11043 11141 11239 11337 11435 11533 11631 11729
## [17545] 11827 11925 12023 12121 12219 12317 12415 12513 12611 12709 12807 12905
## [17557] 13003 13101 13199 13297 13395 13493 13591 13689 13787 13885 13983 14081
## [17569] 14179 14277 14375 14473 14571 14669 14767 14865 14963 15061 15159 15257
## [17581] 15355 15453 15551 15649 15747 15845 15943 16041 16139 16237 16335 16433
## [17593] 16531 16629 16727 16825 16923 17021 17119 17217 17315 17413 17511 17609
## [17605] 17707 17805 17903 18001 18099 18197 18295 18393 18491 18589 18687 18785
## [17617] 18883 18981 19079 19177 19275 19373 19471 19569 19667 19765 19863 19961
## [17629] 20059 20157 20255 20353 20451 20549 20647 20745 20843 20941 21039 21137
## [17641] 21235 21333 21431 21529 21627 21725 21823 21921 22019 22117 22215 22313
## [17653] 22411 22509 22607 22705 22803 22901 22999 23097 23195 23293 23391 23489
## [17665] 23587 23685 23783 23881 23979 24077 24175 24273 24371 24469 24567 24665
## [17677] 24763 24861 24959 25057 25155 25253 25351 25449 25547 25645 25743 25841
## [17689]    68   166   264   362   460   558   656   754   852   950  1048  1146
## [17701]  1244  1342  1440  1538  1636  1734  1832  1930  2028  2126  2224  2322
## [17713]  2420  2518  2616  2714  2812  2910  3008  3106  3204  3302  3400  3498
## [17725]  3596  3694  3792  3890  3988  4086  4184  4282  4380  4478  4576  4674
## [17737]  4772  4870  4968  5066  5164  5262  5360  5458  5556  5654  5752  5850
## [17749]  5948  6046  6144  6242  6340  6438  6536  6634  6732  6830  6928  7026
## [17761]  7124  7222  7320  7418  7516  7614  7712  7810  7908  8006  8104  8202
## [17773]  8300  8398  8496  8594  8692  8790  8888  8986  9084  9182  9280  9378
## [17785]  9476  9574  9672  9770  9868  9966 10064 10162 10260 10358 10456 10554
## [17797] 10652 10750 10848 10946 11044 11142 11240 11338 11436 11534 11632 11730
## [17809] 11828 11926 12024 12122 12220 12318 12416 12514 12612 12710 12808 12906
## [17821] 13004 13102 13200 13298 13396 13494 13592 13690 13788 13886 13984 14082
## [17833] 14180 14278 14376 14474 14572 14670 14768 14866 14964 15062 15160 15258
## [17845] 15356 15454 15552 15650 15748 15846 15944 16042 16140 16238 16336 16434
## [17857] 16532 16630 16728 16826 16924 17022 17120 17218 17316 17414 17512 17610
## [17869] 17708 17806 17904 18002 18100 18198 18296 18394 18492 18590 18688 18786
## [17881] 18884 18982 19080 19178 19276 19374 19472 19570 19668 19766 19864 19962
## [17893] 20060 20158 20256 20354 20452 20550 20648 20746 20844 20942 21040 21138
## [17905] 21236 21334 21432 21530 21628 21726 21824 21922 22020 22118 22216 22314
## [17917] 22412 22510 22608 22706 22804 22902 23000 23098 23196 23294 23392 23490
## [17929] 23588 23686 23784 23882 23980 24078 24176 24274 24372 24470 24568 24666
## [17941] 24764 24862 24960 25058 25156 25254 25352 25450 25548 25646 25744 25842
## [17953]    69   167   265   363   461   559   657   755   853   951  1049  1147
## [17965]  1245  1343  1441  1539  1637  1735  1833  1931  2029  2127  2225  2323
## [17977]  2421  2519  2617  2715  2813  2911  3009  3107  3205  3303  3401  3499
## [17989]  3597  3695  3793  3891  3989  4087  4185  4283  4381  4479  4577  4675
## [18001]  4773  4871  4969  5067  5165  5263  5361  5459  5557  5655  5753  5851
## [18013]  5949  6047  6145  6243  6341  6439  6537  6635  6733  6831  6929  7027
## [18025]  7125  7223  7321  7419  7517  7615  7713  7811  7909  8007  8105  8203
## [18037]  8301  8399  8497  8595  8693  8791  8889  8987  9085  9183  9281  9379
## [18049]  9477  9575  9673  9771  9869  9967 10065 10163 10261 10359 10457 10555
## [18061] 10653 10751 10849 10947 11045 11143 11241 11339 11437 11535 11633 11731
## [18073] 11829 11927 12025 12123 12221 12319 12417 12515 12613 12711 12809 12907
## [18085] 13005 13103 13201 13299 13397 13495 13593 13691 13789 13887 13985 14083
## [18097] 14181 14279 14377 14475 14573 14671 14769 14867 14965 15063 15161 15259
## [18109] 15357 15455 15553 15651 15749 15847 15945 16043 16141 16239 16337 16435
## [18121] 16533 16631 16729 16827 16925 17023 17121 17219 17317 17415 17513 17611
## [18133] 17709 17807 17905 18003 18101 18199 18297 18395 18493 18591 18689 18787
## [18145] 18885 18983 19081 19179 19277 19375 19473 19571 19669 19767 19865 19963
## [18157] 20061 20159 20257 20355 20453 20551 20649 20747 20845 20943 21041 21139
## [18169] 21237 21335 21433 21531 21629 21727 21825 21923 22021 22119 22217 22315
## [18181] 22413 22511 22609 22707 22805 22903 23001 23099 23197 23295 23393 23491
## [18193] 23589 23687 23785 23883 23981 24079 24177 24275 24373 24471 24569 24667
## [18205] 24765 24863 24961 25059 25157 25255 25353 25451 25549 25647 25745 25843
## [18217]    70   168   266   364   462   560   658   756   854   952  1050  1148
## [18229]  1246  1344  1442  1540  1638  1736  1834  1932  2030  2128  2226  2324
## [18241]  2422  2520  2618  2716  2814  2912  3010  3108  3206  3304  3402  3500
## [18253]  3598  3696  3794  3892  3990  4088  4186  4284  4382  4480  4578  4676
## [18265]  4774  4872  4970  5068  5166  5264  5362  5460  5558  5656  5754  5852
## [18277]  5950  6048  6146  6244  6342  6440  6538  6636  6734  6832  6930  7028
## [18289]  7126  7224  7322  7420  7518  7616  7714  7812  7910  8008  8106  8204
## [18301]  8302  8400  8498  8596  8694  8792  8890  8988  9086  9184  9282  9380
## [18313]  9478  9576  9674  9772  9870  9968 10066 10164 10262 10360 10458 10556
## [18325] 10654 10752 10850 10948 11046 11144 11242 11340 11438 11536 11634 11732
## [18337] 11830 11928 12026 12124 12222 12320 12418 12516 12614 12712 12810 12908
## [18349] 13006 13104 13202 13300 13398 13496 13594 13692 13790 13888 13986 14084
## [18361] 14182 14280 14378 14476 14574 14672 14770 14868 14966 15064 15162 15260
## [18373] 15358 15456 15554 15652 15750 15848 15946 16044 16142 16240 16338 16436
## [18385] 16534 16632 16730 16828 16926 17024 17122 17220 17318 17416 17514 17612
## [18397] 17710 17808 17906 18004 18102 18200 18298 18396 18494 18592 18690 18788
## [18409] 18886 18984 19082 19180 19278 19376 19474 19572 19670 19768 19866 19964
## [18421] 20062 20160 20258 20356 20454 20552 20650 20748 20846 20944 21042 21140
## [18433] 21238 21336 21434 21532 21630 21728 21826 21924 22022 22120 22218 22316
## [18445] 22414 22512 22610 22708 22806 22904 23002 23100 23198 23296 23394 23492
## [18457] 23590 23688 23786 23884 23982 24080 24178 24276 24374 24472 24570 24668
## [18469] 24766 24864 24962 25060 25158 25256 25354 25452 25550 25648 25746 25844
## [18481]    71   169   267   365   463   561   659   757   855   953  1051  1149
## [18493]  1247  1345  1443  1541  1639  1737  1835  1933  2031  2129  2227  2325
## [18505]  2423  2521  2619  2717  2815  2913  3011  3109  3207  3305  3403  3501
## [18517]  3599  3697  3795  3893  3991  4089  4187  4285  4383  4481  4579  4677
## [18529]  4775  4873  4971  5069  5167  5265  5363  5461  5559  5657  5755  5853
## [18541]  5951  6049  6147  6245  6343  6441  6539  6637  6735  6833  6931  7029
## [18553]  7127  7225  7323  7421  7519  7617  7715  7813  7911  8009  8107  8205
## [18565]  8303  8401  8499  8597  8695  8793  8891  8989  9087  9185  9283  9381
## [18577]  9479  9577  9675  9773  9871  9969 10067 10165 10263 10361 10459 10557
## [18589] 10655 10753 10851 10949 11047 11145 11243 11341 11439 11537 11635 11733
## [18601] 11831 11929 12027 12125 12223 12321 12419 12517 12615 12713 12811 12909
## [18613] 13007 13105 13203 13301 13399 13497 13595 13693 13791 13889 13987 14085
## [18625] 14183 14281 14379 14477 14575 14673 14771 14869 14967 15065 15163 15261
## [18637] 15359 15457 15555 15653 15751 15849 15947 16045 16143 16241 16339 16437
## [18649] 16535 16633 16731 16829 16927 17025 17123 17221 17319 17417 17515 17613
## [18661] 17711 17809 17907 18005 18103 18201 18299 18397 18495 18593 18691 18789
## [18673] 18887 18985 19083 19181 19279 19377 19475 19573 19671 19769 19867 19965
## [18685] 20063 20161 20259 20357 20455 20553 20651 20749 20847 20945 21043 21141
## [18697] 21239 21337 21435 21533 21631 21729 21827 21925 22023 22121 22219 22317
## [18709] 22415 22513 22611 22709 22807 22905 23003 23101 23199 23297 23395 23493
## [18721] 23591 23689 23787 23885 23983 24081 24179 24277 24375 24473 24571 24669
## [18733] 24767 24865 24963 25061 25159 25257 25355 25453 25551 25649 25747 25845
## [18745]    72   170   268   366   464   562   660   758   856   954  1052  1150
## [18757]  1248  1346  1444  1542  1640  1738  1836  1934  2032  2130  2228  2326
## [18769]  2424  2522  2620  2718  2816  2914  3012  3110  3208  3306  3404  3502
## [18781]  3600  3698  3796  3894  3992  4090  4188  4286  4384  4482  4580  4678
## [18793]  4776  4874  4972  5070  5168  5266  5364  5462  5560  5658  5756  5854
## [18805]  5952  6050  6148  6246  6344  6442  6540  6638  6736  6834  6932  7030
## [18817]  7128  7226  7324  7422  7520  7618  7716  7814  7912  8010  8108  8206
## [18829]  8304  8402  8500  8598  8696  8794  8892  8990  9088  9186  9284  9382
## [18841]  9480  9578  9676  9774  9872  9970 10068 10166 10264 10362 10460 10558
## [18853] 10656 10754 10852 10950 11048 11146 11244 11342 11440 11538 11636 11734
## [18865] 11832 11930 12028 12126 12224 12322 12420 12518 12616 12714 12812 12910
## [18877] 13008 13106 13204 13302 13400 13498 13596 13694 13792 13890 13988 14086
## [18889] 14184 14282 14380 14478 14576 14674 14772 14870 14968 15066 15164 15262
## [18901] 15360 15458 15556 15654 15752 15850 15948 16046 16144 16242 16340 16438
## [18913] 16536 16634 16732 16830 16928 17026 17124 17222 17320 17418 17516 17614
## [18925] 17712 17810 17908 18006 18104 18202 18300 18398 18496 18594 18692 18790
## [18937] 18888 18986 19084 19182 19280 19378 19476 19574 19672 19770 19868 19966
## [18949] 20064 20162 20260 20358 20456 20554 20652 20750 20848 20946 21044 21142
## [18961] 21240 21338 21436 21534 21632 21730 21828 21926 22024 22122 22220 22318
## [18973] 22416 22514 22612 22710 22808 22906 23004 23102 23200 23298 23396 23494
## [18985] 23592 23690 23788 23886 23984 24082 24180 24278 24376 24474 24572 24670
## [18997] 24768 24866 24964 25062 25160 25258 25356 25454 25552 25650 25748 25846
## [19009]    73   171   269   367   465   563   661   759   857   955  1053  1151
## [19021]  1249  1347  1445  1543  1641  1739  1837  1935  2033  2131  2229  2327
## [19033]  2425  2523  2621  2719  2817  2915  3013  3111  3209  3307  3405  3503
## [19045]  3601  3699  3797  3895  3993  4091  4189  4287  4385  4483  4581  4679
## [19057]  4777  4875  4973  5071  5169  5267  5365  5463  5561  5659  5757  5855
## [19069]  5953  6051  6149  6247  6345  6443  6541  6639  6737  6835  6933  7031
## [19081]  7129  7227  7325  7423  7521  7619  7717  7815  7913  8011  8109  8207
## [19093]  8305  8403  8501  8599  8697  8795  8893  8991  9089  9187  9285  9383
## [19105]  9481  9579  9677  9775  9873  9971 10069 10167 10265 10363 10461 10559
## [19117] 10657 10755 10853 10951 11049 11147 11245 11343 11441 11539 11637 11735
## [19129] 11833 11931 12029 12127 12225 12323 12421 12519 12617 12715 12813 12911
## [19141] 13009 13107 13205 13303 13401 13499 13597 13695 13793 13891 13989 14087
## [19153] 14185 14283 14381 14479 14577 14675 14773 14871 14969 15067 15165 15263
## [19165] 15361 15459 15557 15655 15753 15851 15949 16047 16145 16243 16341 16439
## [19177] 16537 16635 16733 16831 16929 17027 17125 17223 17321 17419 17517 17615
## [19189] 17713 17811 17909 18007 18105 18203 18301 18399 18497 18595 18693 18791
## [19201] 18889 18987 19085 19183 19281 19379 19477 19575 19673 19771 19869 19967
## [19213] 20065 20163 20261 20359 20457 20555 20653 20751 20849 20947 21045 21143
## [19225] 21241 21339 21437 21535 21633 21731 21829 21927 22025 22123 22221 22319
## [19237] 22417 22515 22613 22711 22809 22907 23005 23103 23201 23299 23397 23495
## [19249] 23593 23691 23789 23887 23985 24083 24181 24279 24377 24475 24573 24671
## [19261] 24769 24867 24965 25063 25161 25259 25357 25455 25553 25651 25749 25847
## [19273]    74   172   270   368   466   564   662   760   858   956  1054  1152
## [19285]  1250  1348  1446  1544  1642  1740  1838  1936  2034  2132  2230  2328
## [19297]  2426  2524  2622  2720  2818  2916  3014  3112  3210  3308  3406  3504
## [19309]  3602  3700  3798  3896  3994  4092  4190  4288  4386  4484  4582  4680
## [19321]  4778  4876  4974  5072  5170  5268  5366  5464  5562  5660  5758  5856
## [19333]  5954  6052  6150  6248  6346  6444  6542  6640  6738  6836  6934  7032
## [19345]  7130  7228  7326  7424  7522  7620  7718  7816  7914  8012  8110  8208
## [19357]  8306  8404  8502  8600  8698  8796  8894  8992  9090  9188  9286  9384
## [19369]  9482  9580  9678  9776  9874  9972 10070 10168 10266 10364 10462 10560
## [19381] 10658 10756 10854 10952 11050 11148 11246 11344 11442 11540 11638 11736
## [19393] 11834 11932 12030 12128 12226 12324 12422 12520 12618 12716 12814 12912
## [19405] 13010 13108 13206 13304 13402 13500 13598 13696 13794 13892 13990 14088
## [19417] 14186 14284 14382 14480 14578 14676 14774 14872 14970 15068 15166 15264
## [19429] 15362 15460 15558 15656 15754 15852 15950 16048 16146 16244 16342 16440
## [19441] 16538 16636 16734 16832 16930 17028 17126 17224 17322 17420 17518 17616
## [19453] 17714 17812 17910 18008 18106 18204 18302 18400 18498 18596 18694 18792
## [19465] 18890 18988 19086 19184 19282 19380 19478 19576 19674 19772 19870 19968
## [19477] 20066 20164 20262 20360 20458 20556 20654 20752 20850 20948 21046 21144
## [19489] 21242 21340 21438 21536 21634 21732 21830 21928 22026 22124 22222 22320
## [19501] 22418 22516 22614 22712 22810 22908 23006 23104 23202 23300 23398 23496
## [19513] 23594 23692 23790 23888 23986 24084 24182 24280 24378 24476 24574 24672
## [19525] 24770 24868 24966 25064 25162 25260 25358 25456 25554 25652 25750 25848
## [19537]    75   173   271   369   467   565   663   761   859   957  1055  1153
## [19549]  1251  1349  1447  1545  1643  1741  1839  1937  2035  2133  2231  2329
## [19561]  2427  2525  2623  2721  2819  2917  3015  3113  3211  3309  3407  3505
## [19573]  3603  3701  3799  3897  3995  4093  4191  4289  4387  4485  4583  4681
## [19585]  4779  4877  4975  5073  5171  5269  5367  5465  5563  5661  5759  5857
## [19597]  5955  6053  6151  6249  6347  6445  6543  6641  6739  6837  6935  7033
## [19609]  7131  7229  7327  7425  7523  7621  7719  7817  7915  8013  8111  8209
## [19621]  8307  8405  8503  8601  8699  8797  8895  8993  9091  9189  9287  9385
## [19633]  9483  9581  9679  9777  9875  9973 10071 10169 10267 10365 10463 10561
## [19645] 10659 10757 10855 10953 11051 11149 11247 11345 11443 11541 11639 11737
## [19657] 11835 11933 12031 12129 12227 12325 12423 12521 12619 12717 12815 12913
## [19669] 13011 13109 13207 13305 13403 13501 13599 13697 13795 13893 13991 14089
## [19681] 14187 14285 14383 14481 14579 14677 14775 14873 14971 15069 15167 15265
## [19693] 15363 15461 15559 15657 15755 15853 15951 16049 16147 16245 16343 16441
## [19705] 16539 16637 16735 16833 16931 17029 17127 17225 17323 17421 17519 17617
## [19717] 17715 17813 17911 18009 18107 18205 18303 18401 18499 18597 18695 18793
## [19729] 18891 18989 19087 19185 19283 19381 19479 19577 19675 19773 19871 19969
## [19741] 20067 20165 20263 20361 20459 20557 20655 20753 20851 20949 21047 21145
## [19753] 21243 21341 21439 21537 21635 21733 21831 21929 22027 22125 22223 22321
## [19765] 22419 22517 22615 22713 22811 22909 23007 23105 23203 23301 23399 23497
## [19777] 23595 23693 23791 23889 23987 24085 24183 24281 24379 24477 24575 24673
## [19789] 24771 24869 24967 25065 25163 25261 25359 25457 25555 25653 25751 25849
## [19801]    76   174   272   370   468   566   664   762   860   958  1056  1154
## [19813]  1252  1350  1448  1546  1644  1742  1840  1938  2036  2134  2232  2330
## [19825]  2428  2526  2624  2722  2820  2918  3016  3114  3212  3310  3408  3506
## [19837]  3604  3702  3800  3898  3996  4094  4192  4290  4388  4486  4584  4682
## [19849]  4780  4878  4976  5074  5172  5270  5368  5466  5564  5662  5760  5858
## [19861]  5956  6054  6152  6250  6348  6446  6544  6642  6740  6838  6936  7034
## [19873]  7132  7230  7328  7426  7524  7622  7720  7818  7916  8014  8112  8210
## [19885]  8308  8406  8504  8602  8700  8798  8896  8994  9092  9190  9288  9386
## [19897]  9484  9582  9680  9778  9876  9974 10072 10170 10268 10366 10464 10562
## [19909] 10660 10758 10856 10954 11052 11150 11248 11346 11444 11542 11640 11738
## [19921] 11836 11934 12032 12130 12228 12326 12424 12522 12620 12718 12816 12914
## [19933] 13012 13110 13208 13306 13404 13502 13600 13698 13796 13894 13992 14090
## [19945] 14188 14286 14384 14482 14580 14678 14776 14874 14972 15070 15168 15266
## [19957] 15364 15462 15560 15658 15756 15854 15952 16050 16148 16246 16344 16442
## [19969] 16540 16638 16736 16834 16932 17030 17128 17226 17324 17422 17520 17618
## [19981] 17716 17814 17912 18010 18108 18206 18304 18402 18500 18598 18696 18794
## [19993] 18892 18990 19088 19186 19284 19382 19480 19578 19676 19774 19872 19970
## [20005] 20068 20166 20264 20362 20460 20558 20656 20754 20852 20950 21048 21146
## [20017] 21244 21342 21440 21538 21636 21734 21832 21930 22028 22126 22224 22322
## [20029] 22420 22518 22616 22714 22812 22910 23008 23106 23204 23302 23400 23498
## [20041] 23596 23694 23792 23890 23988 24086 24184 24282 24380 24478 24576 24674
## [20053] 24772 24870 24968 25066 25164 25262 25360 25458 25556 25654 25752 25850
## [20065]    77   175   273   371   469   567   665   763   861   959  1057  1155
## [20077]  1253  1351  1449  1547  1645  1743  1841  1939  2037  2135  2233  2331
## [20089]  2429  2527  2625  2723  2821  2919  3017  3115  3213  3311  3409  3507
## [20101]  3605  3703  3801  3899  3997  4095  4193  4291  4389  4487  4585  4683
## [20113]  4781  4879  4977  5075  5173  5271  5369  5467  5565  5663  5761  5859
## [20125]  5957  6055  6153  6251  6349  6447  6545  6643  6741  6839  6937  7035
## [20137]  7133  7231  7329  7427  7525  7623  7721  7819  7917  8015  8113  8211
## [20149]  8309  8407  8505  8603  8701  8799  8897  8995  9093  9191  9289  9387
## [20161]  9485  9583  9681  9779  9877  9975 10073 10171 10269 10367 10465 10563
## [20173] 10661 10759 10857 10955 11053 11151 11249 11347 11445 11543 11641 11739
## [20185] 11837 11935 12033 12131 12229 12327 12425 12523 12621 12719 12817 12915
## [20197] 13013 13111 13209 13307 13405 13503 13601 13699 13797 13895 13993 14091
## [20209] 14189 14287 14385 14483 14581 14679 14777 14875 14973 15071 15169 15267
## [20221] 15365 15463 15561 15659 15757 15855 15953 16051 16149 16247 16345 16443
## [20233] 16541 16639 16737 16835 16933 17031 17129 17227 17325 17423 17521 17619
## [20245] 17717 17815 17913 18011 18109 18207 18305 18403 18501 18599 18697 18795
## [20257] 18893 18991 19089 19187 19285 19383 19481 19579 19677 19775 19873 19971
## [20269] 20069 20167 20265 20363 20461 20559 20657 20755 20853 20951 21049 21147
## [20281] 21245 21343 21441 21539 21637 21735 21833 21931 22029 22127 22225 22323
## [20293] 22421 22519 22617 22715 22813 22911 23009 23107 23205 23303 23401 23499
## [20305] 23597 23695 23793 23891 23989 24087 24185 24283 24381 24479 24577 24675
## [20317] 24773 24871 24969 25067 25165 25263 25361 25459 25557 25655 25753 25851
## [20329]    78   176   274   372   470   568   666   764   862   960  1058  1156
## [20341]  1254  1352  1450  1548  1646  1744  1842  1940  2038  2136  2234  2332
## [20353]  2430  2528  2626  2724  2822  2920  3018  3116  3214  3312  3410  3508
## [20365]  3606  3704  3802  3900  3998  4096  4194  4292  4390  4488  4586  4684
## [20377]  4782  4880  4978  5076  5174  5272  5370  5468  5566  5664  5762  5860
## [20389]  5958  6056  6154  6252  6350  6448  6546  6644  6742  6840  6938  7036
## [20401]  7134  7232  7330  7428  7526  7624  7722  7820  7918  8016  8114  8212
## [20413]  8310  8408  8506  8604  8702  8800  8898  8996  9094  9192  9290  9388
## [20425]  9486  9584  9682  9780  9878  9976 10074 10172 10270 10368 10466 10564
## [20437] 10662 10760 10858 10956 11054 11152 11250 11348 11446 11544 11642 11740
## [20449] 11838 11936 12034 12132 12230 12328 12426 12524 12622 12720 12818 12916
## [20461] 13014 13112 13210 13308 13406 13504 13602 13700 13798 13896 13994 14092
## [20473] 14190 14288 14386 14484 14582 14680 14778 14876 14974 15072 15170 15268
## [20485] 15366 15464 15562 15660 15758 15856 15954 16052 16150 16248 16346 16444
## [20497] 16542 16640 16738 16836 16934 17032 17130 17228 17326 17424 17522 17620
## [20509] 17718 17816 17914 18012 18110 18208 18306 18404 18502 18600 18698 18796
## [20521] 18894 18992 19090 19188 19286 19384 19482 19580 19678 19776 19874 19972
## [20533] 20070 20168 20266 20364 20462 20560 20658 20756 20854 20952 21050 21148
## [20545] 21246 21344 21442 21540 21638 21736 21834 21932 22030 22128 22226 22324
## [20557] 22422 22520 22618 22716 22814 22912 23010 23108 23206 23304 23402 23500
## [20569] 23598 23696 23794 23892 23990 24088 24186 24284 24382 24480 24578 24676
## [20581] 24774 24872 24970 25068 25166 25264 25362 25460 25558 25656 25754 25852
## [20593]    79   177   275   373   471   569   667   765   863   961  1059  1157
## [20605]  1255  1353  1451  1549  1647  1745  1843  1941  2039  2137  2235  2333
## [20617]  2431  2529  2627  2725  2823  2921  3019  3117  3215  3313  3411  3509
## [20629]  3607  3705  3803  3901  3999  4097  4195  4293  4391  4489  4587  4685
## [20641]  4783  4881  4979  5077  5175  5273  5371  5469  5567  5665  5763  5861
## [20653]  5959  6057  6155  6253  6351  6449  6547  6645  6743  6841  6939  7037
## [20665]  7135  7233  7331  7429  7527  7625  7723  7821  7919  8017  8115  8213
## [20677]  8311  8409  8507  8605  8703  8801  8899  8997  9095  9193  9291  9389
## [20689]  9487  9585  9683  9781  9879  9977 10075 10173 10271 10369 10467 10565
## [20701] 10663 10761 10859 10957 11055 11153 11251 11349 11447 11545 11643 11741
## [20713] 11839 11937 12035 12133 12231 12329 12427 12525 12623 12721 12819 12917
## [20725] 13015 13113 13211 13309 13407 13505 13603 13701 13799 13897 13995 14093
## [20737] 14191 14289 14387 14485 14583 14681 14779 14877 14975 15073 15171 15269
## [20749] 15367 15465 15563 15661 15759 15857 15955 16053 16151 16249 16347 16445
## [20761] 16543 16641 16739 16837 16935 17033 17131 17229 17327 17425 17523 17621
## [20773] 17719 17817 17915 18013 18111 18209 18307 18405 18503 18601 18699 18797
## [20785] 18895 18993 19091 19189 19287 19385 19483 19581 19679 19777 19875 19973
## [20797] 20071 20169 20267 20365 20463 20561 20659 20757 20855 20953 21051 21149
## [20809] 21247 21345 21443 21541 21639 21737 21835 21933 22031 22129 22227 22325
## [20821] 22423 22521 22619 22717 22815 22913 23011 23109 23207 23305 23403 23501
## [20833] 23599 23697 23795 23893 23991 24089 24187 24285 24383 24481 24579 24677
## [20845] 24775 24873 24971 25069 25167 25265 25363 25461 25559 25657 25755 25853
## [20857]    80   178   276   374   472   570   668   766   864   962  1060  1158
## [20869]  1256  1354  1452  1550  1648  1746  1844  1942  2040  2138  2236  2334
## [20881]  2432  2530  2628  2726  2824  2922  3020  3118  3216  3314  3412  3510
## [20893]  3608  3706  3804  3902  4000  4098  4196  4294  4392  4490  4588  4686
## [20905]  4784  4882  4980  5078  5176  5274  5372  5470  5568  5666  5764  5862
## [20917]  5960  6058  6156  6254  6352  6450  6548  6646  6744  6842  6940  7038
## [20929]  7136  7234  7332  7430  7528  7626  7724  7822  7920  8018  8116  8214
## [20941]  8312  8410  8508  8606  8704  8802  8900  8998  9096  9194  9292  9390
## [20953]  9488  9586  9684  9782  9880  9978 10076 10174 10272 10370 10468 10566
## [20965] 10664 10762 10860 10958 11056 11154 11252 11350 11448 11546 11644 11742
## [20977] 11840 11938 12036 12134 12232 12330 12428 12526 12624 12722 12820 12918
## [20989] 13016 13114 13212 13310 13408 13506 13604 13702 13800 13898 13996 14094
## [21001] 14192 14290 14388 14486 14584 14682 14780 14878 14976 15074 15172 15270
## [21013] 15368 15466 15564 15662 15760 15858 15956 16054 16152 16250 16348 16446
## [21025] 16544 16642 16740 16838 16936 17034 17132 17230 17328 17426 17524 17622
## [21037] 17720 17818 17916 18014 18112 18210 18308 18406 18504 18602 18700 18798
## [21049] 18896 18994 19092 19190 19288 19386 19484 19582 19680 19778 19876 19974
## [21061] 20072 20170 20268 20366 20464 20562 20660 20758 20856 20954 21052 21150
## [21073] 21248 21346 21444 21542 21640 21738 21836 21934 22032 22130 22228 22326
## [21085] 22424 22522 22620 22718 22816 22914 23012 23110 23208 23306 23404 23502
## [21097] 23600 23698 23796 23894 23992 24090 24188 24286 24384 24482 24580 24678
## [21109] 24776 24874 24972 25070 25168 25266 25364 25462 25560 25658 25756 25854
## [21121]    81   179   277   375   473   571   669   767   865   963  1061  1159
## [21133]  1257  1355  1453  1551  1649  1747  1845  1943  2041  2139  2237  2335
## [21145]  2433  2531  2629  2727  2825  2923  3021  3119  3217  3315  3413  3511
## [21157]  3609  3707  3805  3903  4001  4099  4197  4295  4393  4491  4589  4687
## [21169]  4785  4883  4981  5079  5177  5275  5373  5471  5569  5667  5765  5863
## [21181]  5961  6059  6157  6255  6353  6451  6549  6647  6745  6843  6941  7039
## [21193]  7137  7235  7333  7431  7529  7627  7725  7823  7921  8019  8117  8215
## [21205]  8313  8411  8509  8607  8705  8803  8901  8999  9097  9195  9293  9391
## [21217]  9489  9587  9685  9783  9881  9979 10077 10175 10273 10371 10469 10567
## [21229] 10665 10763 10861 10959 11057 11155 11253 11351 11449 11547 11645 11743
## [21241] 11841 11939 12037 12135 12233 12331 12429 12527 12625 12723 12821 12919
## [21253] 13017 13115 13213 13311 13409 13507 13605 13703 13801 13899 13997 14095
## [21265] 14193 14291 14389 14487 14585 14683 14781 14879 14977 15075 15173 15271
## [21277] 15369 15467 15565 15663 15761 15859 15957 16055 16153 16251 16349 16447
## [21289] 16545 16643 16741 16839 16937 17035 17133 17231 17329 17427 17525 17623
## [21301] 17721 17819 17917 18015 18113 18211 18309 18407 18505 18603 18701 18799
## [21313] 18897 18995 19093 19191 19289 19387 19485 19583 19681 19779 19877 19975
## [21325] 20073 20171 20269 20367 20465 20563 20661 20759 20857 20955 21053 21151
## [21337] 21249 21347 21445 21543 21641 21739 21837 21935 22033 22131 22229 22327
## [21349] 22425 22523 22621 22719 22817 22915 23013 23111 23209 23307 23405 23503
## [21361] 23601 23699 23797 23895 23993 24091 24189 24287 24385 24483 24581 24679
## [21373] 24777 24875 24973 25071 25169 25267 25365 25463 25561 25659 25757 25855
## [21385]    82   180   278   376   474   572   670   768   866   964  1062  1160
## [21397]  1258  1356  1454  1552  1650  1748  1846  1944  2042  2140  2238  2336
## [21409]  2434  2532  2630  2728  2826  2924  3022  3120  3218  3316  3414  3512
## [21421]  3610  3708  3806  3904  4002  4100  4198  4296  4394  4492  4590  4688
## [21433]  4786  4884  4982  5080  5178  5276  5374  5472  5570  5668  5766  5864
## [21445]  5962  6060  6158  6256  6354  6452  6550  6648  6746  6844  6942  7040
## [21457]  7138  7236  7334  7432  7530  7628  7726  7824  7922  8020  8118  8216
## [21469]  8314  8412  8510  8608  8706  8804  8902  9000  9098  9196  9294  9392
## [21481]  9490  9588  9686  9784  9882  9980 10078 10176 10274 10372 10470 10568
## [21493] 10666 10764 10862 10960 11058 11156 11254 11352 11450 11548 11646 11744
## [21505] 11842 11940 12038 12136 12234 12332 12430 12528 12626 12724 12822 12920
## [21517] 13018 13116 13214 13312 13410 13508 13606 13704 13802 13900 13998 14096
## [21529] 14194 14292 14390 14488 14586 14684 14782 14880 14978 15076 15174 15272
## [21541] 15370 15468 15566 15664 15762 15860 15958 16056 16154 16252 16350 16448
## [21553] 16546 16644 16742 16840 16938 17036 17134 17232 17330 17428 17526 17624
## [21565] 17722 17820 17918 18016 18114 18212 18310 18408 18506 18604 18702 18800
## [21577] 18898 18996 19094 19192 19290 19388 19486 19584 19682 19780 19878 19976
## [21589] 20074 20172 20270 20368 20466 20564 20662 20760 20858 20956 21054 21152
## [21601] 21250 21348 21446 21544 21642 21740 21838 21936 22034 22132 22230 22328
## [21613] 22426 22524 22622 22720 22818 22916 23014 23112 23210 23308 23406 23504
## [21625] 23602 23700 23798 23896 23994 24092 24190 24288 24386 24484 24582 24680
## [21637] 24778 24876 24974 25072 25170 25268 25366 25464 25562 25660 25758 25856
## [21649]    83   181   279   377   475   573   671   769   867   965  1063  1161
## [21661]  1259  1357  1455  1553  1651  1749  1847  1945  2043  2141  2239  2337
## [21673]  2435  2533  2631  2729  2827  2925  3023  3121  3219  3317  3415  3513
## [21685]  3611  3709  3807  3905  4003  4101  4199  4297  4395  4493  4591  4689
## [21697]  4787  4885  4983  5081  5179  5277  5375  5473  5571  5669  5767  5865
## [21709]  5963  6061  6159  6257  6355  6453  6551  6649  6747  6845  6943  7041
## [21721]  7139  7237  7335  7433  7531  7629  7727  7825  7923  8021  8119  8217
## [21733]  8315  8413  8511  8609  8707  8805  8903  9001  9099  9197  9295  9393
## [21745]  9491  9589  9687  9785  9883  9981 10079 10177 10275 10373 10471 10569
## [21757] 10667 10765 10863 10961 11059 11157 11255 11353 11451 11549 11647 11745
## [21769] 11843 11941 12039 12137 12235 12333 12431 12529 12627 12725 12823 12921
## [21781] 13019 13117 13215 13313 13411 13509 13607 13705 13803 13901 13999 14097
## [21793] 14195 14293 14391 14489 14587 14685 14783 14881 14979 15077 15175 15273
## [21805] 15371 15469 15567 15665 15763 15861 15959 16057 16155 16253 16351 16449
## [21817] 16547 16645 16743 16841 16939 17037 17135 17233 17331 17429 17527 17625
## [21829] 17723 17821 17919 18017 18115 18213 18311 18409 18507 18605 18703 18801
## [21841] 18899 18997 19095 19193 19291 19389 19487 19585 19683 19781 19879 19977
## [21853] 20075 20173 20271 20369 20467 20565 20663 20761 20859 20957 21055 21153
## [21865] 21251 21349 21447 21545 21643 21741 21839 21937 22035 22133 22231 22329
## [21877] 22427 22525 22623 22721 22819 22917 23015 23113 23211 23309 23407 23505
## [21889] 23603 23701 23799 23897 23995 24093 24191 24289 24387 24485 24583 24681
## [21901] 24779 24877 24975 25073 25171 25269 25367 25465 25563 25661 25759 25857
## [21913]    84   182   280   378   476   574   672   770   868   966  1064  1162
## [21925]  1260  1358  1456  1554  1652  1750  1848  1946  2044  2142  2240  2338
## [21937]  2436  2534  2632  2730  2828  2926  3024  3122  3220  3318  3416  3514
## [21949]  3612  3710  3808  3906  4004  4102  4200  4298  4396  4494  4592  4690
## [21961]  4788  4886  4984  5082  5180  5278  5376  5474  5572  5670  5768  5866
## [21973]  5964  6062  6160  6258  6356  6454  6552  6650  6748  6846  6944  7042
## [21985]  7140  7238  7336  7434  7532  7630  7728  7826  7924  8022  8120  8218
## [21997]  8316  8414  8512  8610  8708  8806  8904  9002  9100  9198  9296  9394
## [22009]  9492  9590  9688  9786  9884  9982 10080 10178 10276 10374 10472 10570
## [22021] 10668 10766 10864 10962 11060 11158 11256 11354 11452 11550 11648 11746
## [22033] 11844 11942 12040 12138 12236 12334 12432 12530 12628 12726 12824 12922
## [22045] 13020 13118 13216 13314 13412 13510 13608 13706 13804 13902 14000 14098
## [22057] 14196 14294 14392 14490 14588 14686 14784 14882 14980 15078 15176 15274
## [22069] 15372 15470 15568 15666 15764 15862 15960 16058 16156 16254 16352 16450
## [22081] 16548 16646 16744 16842 16940 17038 17136 17234 17332 17430 17528 17626
## [22093] 17724 17822 17920 18018 18116 18214 18312 18410 18508 18606 18704 18802
## [22105] 18900 18998 19096 19194 19292 19390 19488 19586 19684 19782 19880 19978
## [22117] 20076 20174 20272 20370 20468 20566 20664 20762 20860 20958 21056 21154
## [22129] 21252 21350 21448 21546 21644 21742 21840 21938 22036 22134 22232 22330
## [22141] 22428 22526 22624 22722 22820 22918 23016 23114 23212 23310 23408 23506
## [22153] 23604 23702 23800 23898 23996 24094 24192 24290 24388 24486 24584 24682
## [22165] 24780 24878 24976 25074 25172 25270 25368 25466 25564 25662 25760 25858
## [22177]    85   183   281   379   477   575   673   771   869   967  1065  1163
## [22189]  1261  1359  1457  1555  1653  1751  1849  1947  2045  2143  2241  2339
## [22201]  2437  2535  2633  2731  2829  2927  3025  3123  3221  3319  3417  3515
## [22213]  3613  3711  3809  3907  4005  4103  4201  4299  4397  4495  4593  4691
## [22225]  4789  4887  4985  5083  5181  5279  5377  5475  5573  5671  5769  5867
## [22237]  5965  6063  6161  6259  6357  6455  6553  6651  6749  6847  6945  7043
## [22249]  7141  7239  7337  7435  7533  7631  7729  7827  7925  8023  8121  8219
## [22261]  8317  8415  8513  8611  8709  8807  8905  9003  9101  9199  9297  9395
## [22273]  9493  9591  9689  9787  9885  9983 10081 10179 10277 10375 10473 10571
## [22285] 10669 10767 10865 10963 11061 11159 11257 11355 11453 11551 11649 11747
## [22297] 11845 11943 12041 12139 12237 12335 12433 12531 12629 12727 12825 12923
## [22309] 13021 13119 13217 13315 13413 13511 13609 13707 13805 13903 14001 14099
## [22321] 14197 14295 14393 14491 14589 14687 14785 14883 14981 15079 15177 15275
## [22333] 15373 15471 15569 15667 15765 15863 15961 16059 16157 16255 16353 16451
## [22345] 16549 16647 16745 16843 16941 17039 17137 17235 17333 17431 17529 17627
## [22357] 17725 17823 17921 18019 18117 18215 18313 18411 18509 18607 18705 18803
## [22369] 18901 18999 19097 19195 19293 19391 19489 19587 19685 19783 19881 19979
## [22381] 20077 20175 20273 20371 20469 20567 20665 20763 20861 20959 21057 21155
## [22393] 21253 21351 21449 21547 21645 21743 21841 21939 22037 22135 22233 22331
## [22405] 22429 22527 22625 22723 22821 22919 23017 23115 23213 23311 23409 23507
## [22417] 23605 23703 23801 23899 23997 24095 24193 24291 24389 24487 24585 24683
## [22429] 24781 24879 24977 25075 25173 25271 25369 25467 25565 25663 25761 25859
## [22441]    86   184   282   380   478   576   674   772   870   968  1066  1164
## [22453]  1262  1360  1458  1556  1654  1752  1850  1948  2046  2144  2242  2340
## [22465]  2438  2536  2634  2732  2830  2928  3026  3124  3222  3320  3418  3516
## [22477]  3614  3712  3810  3908  4006  4104  4202  4300  4398  4496  4594  4692
## [22489]  4790  4888  4986  5084  5182  5280  5378  5476  5574  5672  5770  5868
## [22501]  5966  6064  6162  6260  6358  6456  6554  6652  6750  6848  6946  7044
## [22513]  7142  7240  7338  7436  7534  7632  7730  7828  7926  8024  8122  8220
## [22525]  8318  8416  8514  8612  8710  8808  8906  9004  9102  9200  9298  9396
## [22537]  9494  9592  9690  9788  9886  9984 10082 10180 10278 10376 10474 10572
## [22549] 10670 10768 10866 10964 11062 11160 11258 11356 11454 11552 11650 11748
## [22561] 11846 11944 12042 12140 12238 12336 12434 12532 12630 12728 12826 12924
## [22573] 13022 13120 13218 13316 13414 13512 13610 13708 13806 13904 14002 14100
## [22585] 14198 14296 14394 14492 14590 14688 14786 14884 14982 15080 15178 15276
## [22597] 15374 15472 15570 15668 15766 15864 15962 16060 16158 16256 16354 16452
## [22609] 16550 16648 16746 16844 16942 17040 17138 17236 17334 17432 17530 17628
## [22621] 17726 17824 17922 18020 18118 18216 18314 18412 18510 18608 18706 18804
## [22633] 18902 19000 19098 19196 19294 19392 19490 19588 19686 19784 19882 19980
## [22645] 20078 20176 20274 20372 20470 20568 20666 20764 20862 20960 21058 21156
## [22657] 21254 21352 21450 21548 21646 21744 21842 21940 22038 22136 22234 22332
## [22669] 22430 22528 22626 22724 22822 22920 23018 23116 23214 23312 23410 23508
## [22681] 23606 23704 23802 23900 23998 24096 24194 24292 24390 24488 24586 24684
## [22693] 24782 24880 24978 25076 25174 25272 25370 25468 25566 25664 25762 25860
## [22705]    87   185   283   381   479   577   675   773   871   969  1067  1165
## [22717]  1263  1361  1459  1557  1655  1753  1851  1949  2047  2145  2243  2341
## [22729]  2439  2537  2635  2733  2831  2929  3027  3125  3223  3321  3419  3517
## [22741]  3615  3713  3811  3909  4007  4105  4203  4301  4399  4497  4595  4693
## [22753]  4791  4889  4987  5085  5183  5281  5379  5477  5575  5673  5771  5869
## [22765]  5967  6065  6163  6261  6359  6457  6555  6653  6751  6849  6947  7045
## [22777]  7143  7241  7339  7437  7535  7633  7731  7829  7927  8025  8123  8221
## [22789]  8319  8417  8515  8613  8711  8809  8907  9005  9103  9201  9299  9397
## [22801]  9495  9593  9691  9789  9887  9985 10083 10181 10279 10377 10475 10573
## [22813] 10671 10769 10867 10965 11063 11161 11259 11357 11455 11553 11651 11749
## [22825] 11847 11945 12043 12141 12239 12337 12435 12533 12631 12729 12827 12925
## [22837] 13023 13121 13219 13317 13415 13513 13611 13709 13807 13905 14003 14101
## [22849] 14199 14297 14395 14493 14591 14689 14787 14885 14983 15081 15179 15277
## [22861] 15375 15473 15571 15669 15767 15865 15963 16061 16159 16257 16355 16453
## [22873] 16551 16649 16747 16845 16943 17041 17139 17237 17335 17433 17531 17629
## [22885] 17727 17825 17923 18021 18119 18217 18315 18413 18511 18609 18707 18805
## [22897] 18903 19001 19099 19197 19295 19393 19491 19589 19687 19785 19883 19981
## [22909] 20079 20177 20275 20373 20471 20569 20667 20765 20863 20961 21059 21157
## [22921] 21255 21353 21451 21549 21647 21745 21843 21941 22039 22137 22235 22333
## [22933] 22431 22529 22627 22725 22823 22921 23019 23117 23215 23313 23411 23509
## [22945] 23607 23705 23803 23901 23999 24097 24195 24293 24391 24489 24587 24685
## [22957] 24783 24881 24979 25077 25175 25273 25371 25469 25567 25665 25763 25861
## [22969]    88   186   284   382   480   578   676   774   872   970  1068  1166
## [22981]  1264  1362  1460  1558  1656  1754  1852  1950  2048  2146  2244  2342
## [22993]  2440  2538  2636  2734  2832  2930  3028  3126  3224  3322  3420  3518
## [23005]  3616  3714  3812  3910  4008  4106  4204  4302  4400  4498  4596  4694
## [23017]  4792  4890  4988  5086  5184  5282  5380  5478  5576  5674  5772  5870
## [23029]  5968  6066  6164  6262  6360  6458  6556  6654  6752  6850  6948  7046
## [23041]  7144  7242  7340  7438  7536  7634  7732  7830  7928  8026  8124  8222
## [23053]  8320  8418  8516  8614  8712  8810  8908  9006  9104  9202  9300  9398
## [23065]  9496  9594  9692  9790  9888  9986 10084 10182 10280 10378 10476 10574
## [23077] 10672 10770 10868 10966 11064 11162 11260 11358 11456 11554 11652 11750
## [23089] 11848 11946 12044 12142 12240 12338 12436 12534 12632 12730 12828 12926
## [23101] 13024 13122 13220 13318 13416 13514 13612 13710 13808 13906 14004 14102
## [23113] 14200 14298 14396 14494 14592 14690 14788 14886 14984 15082 15180 15278
## [23125] 15376 15474 15572 15670 15768 15866 15964 16062 16160 16258 16356 16454
## [23137] 16552 16650 16748 16846 16944 17042 17140 17238 17336 17434 17532 17630
## [23149] 17728 17826 17924 18022 18120 18218 18316 18414 18512 18610 18708 18806
## [23161] 18904 19002 19100 19198 19296 19394 19492 19590 19688 19786 19884 19982
## [23173] 20080 20178 20276 20374 20472 20570 20668 20766 20864 20962 21060 21158
## [23185] 21256 21354 21452 21550 21648 21746 21844 21942 22040 22138 22236 22334
## [23197] 22432 22530 22628 22726 22824 22922 23020 23118 23216 23314 23412 23510
## [23209] 23608 23706 23804 23902 24000 24098 24196 24294 24392 24490 24588 24686
## [23221] 24784 24882 24980 25078 25176 25274 25372 25470 25568 25666 25764 25862
## [23233]    89   187   285   383   481   579   677   775   873   971  1069  1167
## [23245]  1265  1363  1461  1559  1657  1755  1853  1951  2049  2147  2245  2343
## [23257]  2441  2539  2637  2735  2833  2931  3029  3127  3225  3323  3421  3519
## [23269]  3617  3715  3813  3911  4009  4107  4205  4303  4401  4499  4597  4695
## [23281]  4793  4891  4989  5087  5185  5283  5381  5479  5577  5675  5773  5871
## [23293]  5969  6067  6165  6263  6361  6459  6557  6655  6753  6851  6949  7047
## [23305]  7145  7243  7341  7439  7537  7635  7733  7831  7929  8027  8125  8223
## [23317]  8321  8419  8517  8615  8713  8811  8909  9007  9105  9203  9301  9399
## [23329]  9497  9595  9693  9791  9889  9987 10085 10183 10281 10379 10477 10575
## [23341] 10673 10771 10869 10967 11065 11163 11261 11359 11457 11555 11653 11751
## [23353] 11849 11947 12045 12143 12241 12339 12437 12535 12633 12731 12829 12927
## [23365] 13025 13123 13221 13319 13417 13515 13613 13711 13809 13907 14005 14103
## [23377] 14201 14299 14397 14495 14593 14691 14789 14887 14985 15083 15181 15279
## [23389] 15377 15475 15573 15671 15769 15867 15965 16063 16161 16259 16357 16455
## [23401] 16553 16651 16749 16847 16945 17043 17141 17239 17337 17435 17533 17631
## [23413] 17729 17827 17925 18023 18121 18219 18317 18415 18513 18611 18709 18807
## [23425] 18905 19003 19101 19199 19297 19395 19493 19591 19689 19787 19885 19983
## [23437] 20081 20179 20277 20375 20473 20571 20669 20767 20865 20963 21061 21159
## [23449] 21257 21355 21453 21551 21649 21747 21845 21943 22041 22139 22237 22335
## [23461] 22433 22531 22629 22727 22825 22923 23021 23119 23217 23315 23413 23511
## [23473] 23609 23707 23805 23903 24001 24099 24197 24295 24393 24491 24589 24687
## [23485] 24785 24883 24981 25079 25177 25275 25373 25471 25569 25667 25765 25863
## [23497]    90   188   286   384   482   580   678   776   874   972  1070  1168
## [23509]  1266  1364  1462  1560  1658  1756  1854  1952  2050  2148  2246  2344
## [23521]  2442  2540  2638  2736  2834  2932  3030  3128  3226  3324  3422  3520
## [23533]  3618  3716  3814  3912  4010  4108  4206  4304  4402  4500  4598  4696
## [23545]  4794  4892  4990  5088  5186  5284  5382  5480  5578  5676  5774  5872
## [23557]  5970  6068  6166  6264  6362  6460  6558  6656  6754  6852  6950  7048
## [23569]  7146  7244  7342  7440  7538  7636  7734  7832  7930  8028  8126  8224
## [23581]  8322  8420  8518  8616  8714  8812  8910  9008  9106  9204  9302  9400
## [23593]  9498  9596  9694  9792  9890  9988 10086 10184 10282 10380 10478 10576
## [23605] 10674 10772 10870 10968 11066 11164 11262 11360 11458 11556 11654 11752
## [23617] 11850 11948 12046 12144 12242 12340 12438 12536 12634 12732 12830 12928
## [23629] 13026 13124 13222 13320 13418 13516 13614 13712 13810 13908 14006 14104
## [23641] 14202 14300 14398 14496 14594 14692 14790 14888 14986 15084 15182 15280
## [23653] 15378 15476 15574 15672 15770 15868 15966 16064 16162 16260 16358 16456
## [23665] 16554 16652 16750 16848 16946 17044 17142 17240 17338 17436 17534 17632
## [23677] 17730 17828 17926 18024 18122 18220 18318 18416 18514 18612 18710 18808
## [23689] 18906 19004 19102 19200 19298 19396 19494 19592 19690 19788 19886 19984
## [23701] 20082 20180 20278 20376 20474 20572 20670 20768 20866 20964 21062 21160
## [23713] 21258 21356 21454 21552 21650 21748 21846 21944 22042 22140 22238 22336
## [23725] 22434 22532 22630 22728 22826 22924 23022 23120 23218 23316 23414 23512
## [23737] 23610 23708 23806 23904 24002 24100 24198 24296 24394 24492 24590 24688
## [23749] 24786 24884 24982 25080 25178 25276 25374 25472 25570 25668 25766 25864
## [23761]    91   189   287   385   483   581   679   777   875   973  1071  1169
## [23773]  1267  1365  1463  1561  1659  1757  1855  1953  2051  2149  2247  2345
## [23785]  2443  2541  2639  2737  2835  2933  3031  3129  3227  3325  3423  3521
## [23797]  3619  3717  3815  3913  4011  4109  4207  4305  4403  4501  4599  4697
## [23809]  4795  4893  4991  5089  5187  5285  5383  5481  5579  5677  5775  5873
## [23821]  5971  6069  6167  6265  6363  6461  6559  6657  6755  6853  6951  7049
## [23833]  7147  7245  7343  7441  7539  7637  7735  7833  7931  8029  8127  8225
## [23845]  8323  8421  8519  8617  8715  8813  8911  9009  9107  9205  9303  9401
## [23857]  9499  9597  9695  9793  9891  9989 10087 10185 10283 10381 10479 10577
## [23869] 10675 10773 10871 10969 11067 11165 11263 11361 11459 11557 11655 11753
## [23881] 11851 11949 12047 12145 12243 12341 12439 12537 12635 12733 12831 12929
## [23893] 13027 13125 13223 13321 13419 13517 13615 13713 13811 13909 14007 14105
## [23905] 14203 14301 14399 14497 14595 14693 14791 14889 14987 15085 15183 15281
## [23917] 15379 15477 15575 15673 15771 15869 15967 16065 16163 16261 16359 16457
## [23929] 16555 16653 16751 16849 16947 17045 17143 17241 17339 17437 17535 17633
## [23941] 17731 17829 17927 18025 18123 18221 18319 18417 18515 18613 18711 18809
## [23953] 18907 19005 19103 19201 19299 19397 19495 19593 19691 19789 19887 19985
## [23965] 20083 20181 20279 20377 20475 20573 20671 20769 20867 20965 21063 21161
## [23977] 21259 21357 21455 21553 21651 21749 21847 21945 22043 22141 22239 22337
## [23989] 22435 22533 22631 22729 22827 22925 23023 23121 23219 23317 23415 23513
## [24001] 23611 23709 23807 23905 24003 24101 24199 24297 24395 24493 24591 24689
## [24013] 24787 24885 24983 25081 25179 25277 25375 25473 25571 25669 25767 25865
## [24025]    92   190   288   386   484   582   680   778   876   974  1072  1170
## [24037]  1268  1366  1464  1562  1660  1758  1856  1954  2052  2150  2248  2346
## [24049]  2444  2542  2640  2738  2836  2934  3032  3130  3228  3326  3424  3522
## [24061]  3620  3718  3816  3914  4012  4110  4208  4306  4404  4502  4600  4698
## [24073]  4796  4894  4992  5090  5188  5286  5384  5482  5580  5678  5776  5874
## [24085]  5972  6070  6168  6266  6364  6462  6560  6658  6756  6854  6952  7050
## [24097]  7148  7246  7344  7442  7540  7638  7736  7834  7932  8030  8128  8226
## [24109]  8324  8422  8520  8618  8716  8814  8912  9010  9108  9206  9304  9402
## [24121]  9500  9598  9696  9794  9892  9990 10088 10186 10284 10382 10480 10578
## [24133] 10676 10774 10872 10970 11068 11166 11264 11362 11460 11558 11656 11754
## [24145] 11852 11950 12048 12146 12244 12342 12440 12538 12636 12734 12832 12930
## [24157] 13028 13126 13224 13322 13420 13518 13616 13714 13812 13910 14008 14106
## [24169] 14204 14302 14400 14498 14596 14694 14792 14890 14988 15086 15184 15282
## [24181] 15380 15478 15576 15674 15772 15870 15968 16066 16164 16262 16360 16458
## [24193] 16556 16654 16752 16850 16948 17046 17144 17242 17340 17438 17536 17634
## [24205] 17732 17830 17928 18026 18124 18222 18320 18418 18516 18614 18712 18810
## [24217] 18908 19006 19104 19202 19300 19398 19496 19594 19692 19790 19888 19986
## [24229] 20084 20182 20280 20378 20476 20574 20672 20770 20868 20966 21064 21162
## [24241] 21260 21358 21456 21554 21652 21750 21848 21946 22044 22142 22240 22338
## [24253] 22436 22534 22632 22730 22828 22926 23024 23122 23220 23318 23416 23514
## [24265] 23612 23710 23808 23906 24004 24102 24200 24298 24396 24494 24592 24690
## [24277] 24788 24886 24984 25082 25180 25278 25376 25474 25572 25670 25768 25866
## [24289]    93   191   289   387   485   583   681   779   877   975  1073  1171
## [24301]  1269  1367  1465  1563  1661  1759  1857  1955  2053  2151  2249  2347
## [24313]  2445  2543  2641  2739  2837  2935  3033  3131  3229  3327  3425  3523
## [24325]  3621  3719  3817  3915  4013  4111  4209  4307  4405  4503  4601  4699
## [24337]  4797  4895  4993  5091  5189  5287  5385  5483  5581  5679  5777  5875
## [24349]  5973  6071  6169  6267  6365  6463  6561  6659  6757  6855  6953  7051
## [24361]  7149  7247  7345  7443  7541  7639  7737  7835  7933  8031  8129  8227
## [24373]  8325  8423  8521  8619  8717  8815  8913  9011  9109  9207  9305  9403
## [24385]  9501  9599  9697  9795  9893  9991 10089 10187 10285 10383 10481 10579
## [24397] 10677 10775 10873 10971 11069 11167 11265 11363 11461 11559 11657 11755
## [24409] 11853 11951 12049 12147 12245 12343 12441 12539 12637 12735 12833 12931
## [24421] 13029 13127 13225 13323 13421 13519 13617 13715 13813 13911 14009 14107
## [24433] 14205 14303 14401 14499 14597 14695 14793 14891 14989 15087 15185 15283
## [24445] 15381 15479 15577 15675 15773 15871 15969 16067 16165 16263 16361 16459
## [24457] 16557 16655 16753 16851 16949 17047 17145 17243 17341 17439 17537 17635
## [24469] 17733 17831 17929 18027 18125 18223 18321 18419 18517 18615 18713 18811
## [24481] 18909 19007 19105 19203 19301 19399 19497 19595 19693 19791 19889 19987
## [24493] 20085 20183 20281 20379 20477 20575 20673 20771 20869 20967 21065 21163
## [24505] 21261 21359 21457 21555 21653 21751 21849 21947 22045 22143 22241 22339
## [24517] 22437 22535 22633 22731 22829 22927 23025 23123 23221 23319 23417 23515
## [24529] 23613 23711 23809 23907 24005 24103 24201 24299 24397 24495 24593 24691
## [24541] 24789 24887 24985 25083 25181 25279 25377 25475 25573 25671 25769 25867
## [24553]    94   192   290   388   486   584   682   780   878   976  1074  1172
## [24565]  1270  1368  1466  1564  1662  1760  1858  1956  2054  2152  2250  2348
## [24577]  2446  2544  2642  2740  2838  2936  3034  3132  3230  3328  3426  3524
## [24589]  3622  3720  3818  3916  4014  4112  4210  4308  4406  4504  4602  4700
## [24601]  4798  4896  4994  5092  5190  5288  5386  5484  5582  5680  5778  5876
## [24613]  5974  6072  6170  6268  6366  6464  6562  6660  6758  6856  6954  7052
## [24625]  7150  7248  7346  7444  7542  7640  7738  7836  7934  8032  8130  8228
## [24637]  8326  8424  8522  8620  8718  8816  8914  9012  9110  9208  9306  9404
## [24649]  9502  9600  9698  9796  9894  9992 10090 10188 10286 10384 10482 10580
## [24661] 10678 10776 10874 10972 11070 11168 11266 11364 11462 11560 11658 11756
## [24673] 11854 11952 12050 12148 12246 12344 12442 12540 12638 12736 12834 12932
## [24685] 13030 13128 13226 13324 13422 13520 13618 13716 13814 13912 14010 14108
## [24697] 14206 14304 14402 14500 14598 14696 14794 14892 14990 15088 15186 15284
## [24709] 15382 15480 15578 15676 15774 15872 15970 16068 16166 16264 16362 16460
## [24721] 16558 16656 16754 16852 16950 17048 17146 17244 17342 17440 17538 17636
## [24733] 17734 17832 17930 18028 18126 18224 18322 18420 18518 18616 18714 18812
## [24745] 18910 19008 19106 19204 19302 19400 19498 19596 19694 19792 19890 19988
## [24757] 20086 20184 20282 20380 20478 20576 20674 20772 20870 20968 21066 21164
## [24769] 21262 21360 21458 21556 21654 21752 21850 21948 22046 22144 22242 22340
## [24781] 22438 22536 22634 22732 22830 22928 23026 23124 23222 23320 23418 23516
## [24793] 23614 23712 23810 23908 24006 24104 24202 24300 24398 24496 24594 24692
## [24805] 24790 24888 24986 25084 25182 25280 25378 25476 25574 25672 25770 25868
## [24817]    95   193   291   389   487   585   683   781   879   977  1075  1173
## [24829]  1271  1369  1467  1565  1663  1761  1859  1957  2055  2153  2251  2349
## [24841]  2447  2545  2643  2741  2839  2937  3035  3133  3231  3329  3427  3525
## [24853]  3623  3721  3819  3917  4015  4113  4211  4309  4407  4505  4603  4701
## [24865]  4799  4897  4995  5093  5191  5289  5387  5485  5583  5681  5779  5877
## [24877]  5975  6073  6171  6269  6367  6465  6563  6661  6759  6857  6955  7053
## [24889]  7151  7249  7347  7445  7543  7641  7739  7837  7935  8033  8131  8229
## [24901]  8327  8425  8523  8621  8719  8817  8915  9013  9111  9209  9307  9405
## [24913]  9503  9601  9699  9797  9895  9993 10091 10189 10287 10385 10483 10581
## [24925] 10679 10777 10875 10973 11071 11169 11267 11365 11463 11561 11659 11757
## [24937] 11855 11953 12051 12149 12247 12345 12443 12541 12639 12737 12835 12933
## [24949] 13031 13129 13227 13325 13423 13521 13619 13717 13815 13913 14011 14109
## [24961] 14207 14305 14403 14501 14599 14697 14795 14893 14991 15089 15187 15285
## [24973] 15383 15481 15579 15677 15775 15873 15971 16069 16167 16265 16363 16461
## [24985] 16559 16657 16755 16853 16951 17049 17147 17245 17343 17441 17539 17637
## [24997] 17735 17833 17931 18029 18127 18225 18323 18421 18519 18617 18715 18813
## [25009] 18911 19009 19107 19205 19303 19401 19499 19597 19695 19793 19891 19989
## [25021] 20087 20185 20283 20381 20479 20577 20675 20773 20871 20969 21067 21165
## [25033] 21263 21361 21459 21557 21655 21753 21851 21949 22047 22145 22243 22341
## [25045] 22439 22537 22635 22733 22831 22929 23027 23125 23223 23321 23419 23517
## [25057] 23615 23713 23811 23909 24007 24105 24203 24301 24399 24497 24595 24693
## [25069] 24791 24889 24987 25085 25183 25281 25379 25477 25575 25673 25771 25869
## [25081]    96   194   292   390   488   586   684   782   880   978  1076  1174
## [25093]  1272  1370  1468  1566  1664  1762  1860  1958  2056  2154  2252  2350
## [25105]  2448  2546  2644  2742  2840  2938  3036  3134  3232  3330  3428  3526
## [25117]  3624  3722  3820  3918  4016  4114  4212  4310  4408  4506  4604  4702
## [25129]  4800  4898  4996  5094  5192  5290  5388  5486  5584  5682  5780  5878
## [25141]  5976  6074  6172  6270  6368  6466  6564  6662  6760  6858  6956  7054
## [25153]  7152  7250  7348  7446  7544  7642  7740  7838  7936  8034  8132  8230
## [25165]  8328  8426  8524  8622  8720  8818  8916  9014  9112  9210  9308  9406
## [25177]  9504  9602  9700  9798  9896  9994 10092 10190 10288 10386 10484 10582
## [25189] 10680 10778 10876 10974 11072 11170 11268 11366 11464 11562 11660 11758
## [25201] 11856 11954 12052 12150 12248 12346 12444 12542 12640 12738 12836 12934
## [25213] 13032 13130 13228 13326 13424 13522 13620 13718 13816 13914 14012 14110
## [25225] 14208 14306 14404 14502 14600 14698 14796 14894 14992 15090 15188 15286
## [25237] 15384 15482 15580 15678 15776 15874 15972 16070 16168 16266 16364 16462
## [25249] 16560 16658 16756 16854 16952 17050 17148 17246 17344 17442 17540 17638
## [25261] 17736 17834 17932 18030 18128 18226 18324 18422 18520 18618 18716 18814
## [25273] 18912 19010 19108 19206 19304 19402 19500 19598 19696 19794 19892 19990
## [25285] 20088 20186 20284 20382 20480 20578 20676 20774 20872 20970 21068 21166
## [25297] 21264 21362 21460 21558 21656 21754 21852 21950 22048 22146 22244 22342
## [25309] 22440 22538 22636 22734 22832 22930 23028 23126 23224 23322 23420 23518
## [25321] 23616 23714 23812 23910 24008 24106 24204 24302 24400 24498 24596 24694
## [25333] 24792 24890 24988 25086 25184 25282 25380 25478 25576 25674 25772 25870
## [25345]    97   195   293   391   489   587   685   783   881   979  1077  1175
## [25357]  1273  1371  1469  1567  1665  1763  1861  1959  2057  2155  2253  2351
## [25369]  2449  2547  2645  2743  2841  2939  3037  3135  3233  3331  3429  3527
## [25381]  3625  3723  3821  3919  4017  4115  4213  4311  4409  4507  4605  4703
## [25393]  4801  4899  4997  5095  5193  5291  5389  5487  5585  5683  5781  5879
## [25405]  5977  6075  6173  6271  6369  6467  6565  6663  6761  6859  6957  7055
## [25417]  7153  7251  7349  7447  7545  7643  7741  7839  7937  8035  8133  8231
## [25429]  8329  8427  8525  8623  8721  8819  8917  9015  9113  9211  9309  9407
## [25441]  9505  9603  9701  9799  9897  9995 10093 10191 10289 10387 10485 10583
## [25453] 10681 10779 10877 10975 11073 11171 11269 11367 11465 11563 11661 11759
## [25465] 11857 11955 12053 12151 12249 12347 12445 12543 12641 12739 12837 12935
## [25477] 13033 13131 13229 13327 13425 13523 13621 13719 13817 13915 14013 14111
## [25489] 14209 14307 14405 14503 14601 14699 14797 14895 14993 15091 15189 15287
## [25501] 15385 15483 15581 15679 15777 15875 15973 16071 16169 16267 16365 16463
## [25513] 16561 16659 16757 16855 16953 17051 17149 17247 17345 17443 17541 17639
## [25525] 17737 17835 17933 18031 18129 18227 18325 18423 18521 18619 18717 18815
## [25537] 18913 19011 19109 19207 19305 19403 19501 19599 19697 19795 19893 19991
## [25549] 20089 20187 20285 20383 20481 20579 20677 20775 20873 20971 21069 21167
## [25561] 21265 21363 21461 21559 21657 21755 21853 21951 22049 22147 22245 22343
## [25573] 22441 22539 22637 22735 22833 22931 23029 23127 23225 23323 23421 23519
## [25585] 23617 23715 23813 23911 24009 24107 24205 24303 24401 24499 24597 24695
## [25597] 24793 24891 24989 25087 25185 25283 25381 25479 25577 25675 25773 25871
## [25609]    98   196   294   392   490   588   686   784   882   980  1078  1176
## [25621]  1274  1372  1470  1568  1666  1764  1862  1960  2058  2156  2254  2352
## [25633]  2450  2548  2646  2744  2842  2940  3038  3136  3234  3332  3430  3528
## [25645]  3626  3724  3822  3920  4018  4116  4214  4312  4410  4508  4606  4704
## [25657]  4802  4900  4998  5096  5194  5292  5390  5488  5586  5684  5782  5880
## [25669]  5978  6076  6174  6272  6370  6468  6566  6664  6762  6860  6958  7056
## [25681]  7154  7252  7350  7448  7546  7644  7742  7840  7938  8036  8134  8232
## [25693]  8330  8428  8526  8624  8722  8820  8918  9016  9114  9212  9310  9408
## [25705]  9506  9604  9702  9800  9898  9996 10094 10192 10290 10388 10486 10584
## [25717] 10682 10780 10878 10976 11074 11172 11270 11368 11466 11564 11662 11760
## [25729] 11858 11956 12054 12152 12250 12348 12446 12544 12642 12740 12838 12936
## [25741] 13034 13132 13230 13328 13426 13524 13622 13720 13818 13916 14014 14112
## [25753] 14210 14308 14406 14504 14602 14700 14798 14896 14994 15092 15190 15288
## [25765] 15386 15484 15582 15680 15778 15876 15974 16072 16170 16268 16366 16464
## [25777] 16562 16660 16758 16856 16954 17052 17150 17248 17346 17444 17542 17640
## [25789] 17738 17836 17934 18032 18130 18228 18326 18424 18522 18620 18718 18816
## [25801] 18914 19012 19110 19208 19306 19404 19502 19600 19698 19796 19894 19992
## [25813] 20090 20188 20286 20384 20482 20580 20678 20776 20874 20972 21070 21168
## [25825] 21266 21364 21462 21560 21658 21756 21854 21952 22050 22148 22246 22344
## [25837] 22442 22540 22638 22736 22834 22932 23030 23128 23226 23324 23422 23520
## [25849] 23618 23716 23814 23912 24010 24108 24206 24304 24402 24500 24598 24696
## [25861] 24794 24892 24990 25088 25186 25284 25382 25480 25578 25676 25774 25872

# I then merge the cleaned deceased and confirmed datasets.
Merged_Data <- cbind(Confirmed_Long, Deceased_Long)
colnames(Merged_Data)

##  [1] "Province/State"  "Country/Region"  "Lat"             "Long"           
##  [5] "Dates"           "Confirmed_Cases" "COUNTRY"         "Date"           
##  [9] "Province/State"  "Country/Region"  "Lat"             "Long"           
## [13] "Dates"           "Deceased"        "COUNTRY"         "Date"

# I only keep the variables we are interested in.
myvars <- c("COUNTRY", "Province/State", "Lat", "Long", "Dates", "Confirmed_Cases", "Date", "Deceased")

# I rename this file as our temporary data.
Temporary_Data_Country <- Merged_Data[myvars]

# We might also want a dataset with the total number of global confirmed cases and deaths per day.
World_Data <- Temporary_Data_Country %>% group_by(Date) %>%
summarise(country='World', Global_Confirmed_Cases = sum(Confirmed_Cases, na.rm=T),
Global_Deceased = sum(Deceased, na.rm=T))
World_Data$Death_Rate <- ((World_Data$Global_Deceased/World_Data$Global_Confirmed_Cases) * 100)

Collapsing and calculating relevant variables in the temporary data.

# Here, I subset the data so as to properly collapse by country.
myvarstemporary <- c("COUNTRY", "Date", "Confirmed_Cases", "Deceased")

Temporary_Data_Country <- Temporary_Data_Country[myvarstemporary]

Temporary_Data_Country <- Temporary_Data_Country %>%
  group_by(COUNTRY, Date) %>%
  summarise(Total_Cases_Country = sum(Confirmed_Cases), Total_Deceased_Country = sum(Deceased))

# I next want to calculate the increase in deaths and confirmed cases per day.

Temporary_Data_Country %>%
  arrange(COUNTRY, Date)

## # A tibble: 18,130 x 4
## # Groups:   COUNTRY [185]
##    COUNTRY     Date       Total_Cases_Country Total_Deceased_Country
##    <chr>       <date>                   <dbl>                  <dbl>
##  1 Afghanistan 2020-01-22                   0                      0
##  2 Afghanistan 2020-01-23                   0                      0
##  3 Afghanistan 2020-01-24                   0                      0
##  4 Afghanistan 2020-01-25                   0                      0
##  5 Afghanistan 2020-01-26                   0                      0
##  6 Afghanistan 2020-01-27                   0                      0
##  7 Afghanistan 2020-01-28                   0                      0
##  8 Afghanistan 2020-01-29                   0                      0
##  9 Afghanistan 2020-01-30                   0                      0
## 10 Afghanistan 2020-01-31                   0                      0
## # … with 18,120 more rows

number <- nrow(Temporary_Data_Country)
First_Day <- min(Temporary_Data_Country$Date)

Temporary_Data_Country$New_Confirmed_Country <- ifelse(Temporary_Data_Country$Date == First_Day, NA, Temporary_Data_Country$Total_Cases_Country - dplyr::lag(Temporary_Data_Country$Total_Cases_Country, number=1))

Temporary_Data_Country$New_Total_Deceased_Country <- ifelse(Temporary_Data_Country$Date == First_Day, NA, Temporary_Data_Country$Total_Deceased_Country - dplyr::lag(Temporary_Data_Country$Total_Deceased_Country, number=1))

# There are a few values for which we have negative new deaths and new cases. Intuitively, this shouldn't make sense. This is actually a problem which steps from the COVID data. For example, Iceland has some weird values in the initial dataframe. Although deaths should represent cumulative sums, if we observe the data from 3/15 - 4/05, we note that the number of recorded deaths drops from 5 to 0, then increases back to six as the month progresses. This is a well-documented problem which stems from the original Github data: https://github.com/CSSEGISandData/COVID-19/issues/2379; https://github.com/CSSEGISandData/COVID-19/issues/2165. We can either exclude data points which show a decrease in cumulative deaths (which I haven't done here); or wait for the data to be updated. Because we only drop a few observations from the analysis, I choose to exclude them, and proceed as normal. Note that when we take global sums, this will affect our cumulative totals (as we have excluded observations for certain nations on certain days).

Temporary_Data_Country <- Temporary_Data_Country[which(Temporary_Data_Country$New_Confirmed_Country >= 0),]
Temporary_Data_Country <- Temporary_Data_Country[which(Temporary_Data_Country$New_Total_Deceased_Country >= 0),]

# Now, I calculate the total mortality rate per capita, new mortality rate per capita, death rate per capita, and confirmed cases per capita (using data from the United Nations on global demographics for the merge). 

UN_Population <- read_csv("data/UN_Population_Data.csv")

## Parsed with column specification:
## cols(
##   Location = col_character(),
##   Population = col_double()
## )

UN_Population$COUNTRY <- UN_Population$Location

UN_Population[25, "COUNTRY"] <- "Bolivia"
UN_Population[30, "COUNTRY"] <- "Brunei"
UN_Population[43, "COUNTRY"] <- "Taiwan*"
UN_Population[46, "COUNTRY"] <- "Congo (Brazzaville)"
UN_Population[54, "COUNTRY"] <-"Cote d'Ivoire"
UN_Population[56, "COUNTRY"] <- "Congo (Kinshasa)"
UN_Population[98, "COUNTRY"] <- "Iran"
UN_Population[112, "COUNTRY"] <- "Laos"
UN_Population[138, "COUNTRY"] <- "Burma"
UN_Population[162, "COUNTRY"] <- "Korea, South"
UN_Population[163, "COUNTRY"] <- "Moldova"
UN_Population[165, "COUNTRY"] <- "Russia"
UN_Population[196, "COUNTRY"] <- "Syria"
UN_Population[212, "COUNTRY"] <- "Tanzania"
UN_Population[213, "COUNTRY"] <- "US"
UN_Population[217, "COUNTRY"] <- "Venezuela"
UN_Population[218, "COUNTRY"] <- "Vietnam"

# I subset the file to only include the nations in our final data.
UN_Population <- subset(UN_Population, is.element(UN_Population$COUNTRY, Temporary_Data_Country$COUNTRY))

# Lastly, I join the two dataframes. Note, we lose some observations using inner join (Western Sahara, West Bank and Gaza, MS Zaandam, Kosovo, Diamond Princess, and the Central African Republic.). However, we are not interested in these nations for the purposes of our sample.
Temporary_Data_Country <- inner_join(Temporary_Data_Country, UN_Population)

## Joining, by = "COUNTRY"

# This allows me to calculate our variable of interest: the mortality rate per capita (for both total deaths, and new deaths).
Temporary_Data_Country$Total_Mortality_Rate_Per_Capita = (Temporary_Data_Country$Total_Deceased_Country / Temporary_Data_Country$Population)

Temporary_Data_Country$New_Mortality_Rate_Per_Capita = (Temporary_Data_Country$New_Total_Deceased_Country / Temporary_Data_Country$Population)

# We might also be interested in cases per capita.
Temporary_Data_Country$Total_Cases_Country_Per_Capita = (Temporary_Data_Country$Total_Cases_Country / Temporary_Data_Country$Population)

# Lastly, I want to create a rolling average of the total and new mortality rates per capita, new confirmed cases, and new deaths. This is a bit complicated, so bear with me.

# First, I order the data by country and date.
Temporary_Data_Country <- Temporary_Data_Country[order(Temporary_Data_Country$COUNTRY, Temporary_Data_Country$Date),]

# I then construct a seven-day rolling average of confirmed cases, deaths, and both types of mortality rates by country.
Temporary_Data_Country <- Temporary_Data_Country %>%
  group_by(COUNTRY) %>%
  mutate(rolling_average_confirmed = frollmean(New_Confirmed_Country, 7))
Temporary_Data_Country <- Temporary_Data_Country %>%
  group_by(COUNTRY) %>%
  mutate(rolling_average_deceased= frollmean(New_Total_Deceased_Country, 7))
Temporary_Data_Country <- Temporary_Data_Country %>%
  group_by(COUNTRY) %>%
  mutate(total_rolling_average_mortality = frollmean(Total_Mortality_Rate_Per_Capita, 7))
Temporary_Data_Country <- Temporary_Data_Country %>%
  group_by(COUNTRY) %>%
  mutate(new_rolling_average_mortality = frollmean(New_Mortality_Rate_Per_Capita, 7))

# The seven-day rolling average works well, but the algorithm only gives an output after the first week (as we need seven prior values for the sake of our calculation). Ideally, we would want these missing values to have their own rolling averages (i.e. a rolling average of two days for the second date in our dataset; or a rolling average of five days for the fifth date). To do this, I first create a new dataframe with our missing values from the initial seven-day rolling average.
Missing_Mean_Confirmed <- Temporary_Data_Country[which(is.na(Temporary_Data_Country$rolling_average_confirmed)), ]
Missing_Mean_Total_Deceased_Country <- Temporary_Data_Country[which(is.na(Temporary_Data_Country$rolling_average_deceased)), ]
Missing_Mean_Total_Mortality <- Temporary_Data_Country[which(is.na(Temporary_Data_Country$total_rolling_average_mortality)), ]
Missing_Mean_New_Mortality <- Temporary_Data_Country[which(is.na(Temporary_Data_Country$new_rolling_average_mortality)), ]

# Next, I calculate a "new" rolling average for each of our nations, using the roll apply function. The syntax is complicated; but essentially this command allows the user the freedom to vary rolling averages by different "windows" of dates (i.e. three-day average vs. six-day average).
Missing_Mean_Confirmed <- Missing_Mean_Confirmed %>% 
  group_by(COUNTRY) %>%
  mutate(rolling_average_confirmed2 = rollapply(New_Confirmed_Country, 6, mean, na.rm = TRUE, fill = NA, align = 'right', partial=TRUE))
Missing_Mean_Total_Deceased_Country <- Missing_Mean_Total_Deceased_Country %>% 
  group_by(COUNTRY) %>%
  mutate(rolling_average_deceased2 = rollapply(New_Total_Deceased_Country, 6, mean, na.rm = TRUE, fill = NA, align = 'right', partial=TRUE))
Missing_Mean_Total_Mortality <- Missing_Mean_Total_Mortality %>% 
  group_by(COUNTRY) %>%
  mutate(total_rolling_average_mortality2 = rollapply(Total_Mortality_Rate_Per_Capita, 6, mean, na.rm = TRUE, fill = NA, align = 'right', partial=TRUE))
Missing_Mean_New_Mortality <- Missing_Mean_New_Mortality %>% 
  group_by(COUNTRY) %>%
  mutate(new_rolling_average_mortality2 = rollapply(New_Mortality_Rate_Per_Capita, 6, mean, na.rm = TRUE, fill = NA, align = 'right', partial=TRUE))

# We now want to merge this data back to our temporary file.
Temporary_Data_Country <- merge(Temporary_Data_Country, Missing_Mean_Confirmed, by = c("COUNTRY", "Date"), all=TRUE)
Temporary_Data_Country <- merge(Temporary_Data_Country, Missing_Mean_Total_Deceased_Country, by = c("COUNTRY", "Date"), all=TRUE)
Temporary_Data_Country <- merge(Temporary_Data_Country, Missing_Mean_Total_Mortality, by = c("COUNTRY", "Date"), all=TRUE)

## Warning in merge.data.frame(Temporary_Data_Country,
## Missing_Mean_Total_Mortality, : column names 'Total_Cases_Country.x',
## 'Total_Deceased_Country.x', 'New_Confirmed_Country.x',
## 'New_Total_Deceased_Country.x', 'Location.x', 'Population.x',
## 'Total_Mortality_Rate_Per_Capita.x', 'New_Mortality_Rate_Per_Capita.x',
## 'Total_Cases_Country_Per_Capita.x', 'rolling_average_confirmed.x',
## 'rolling_average_deceased.x', 'total_rolling_average_mortality.x',
## 'new_rolling_average_mortality.x', 'Total_Cases_Country.y',
## 'Total_Deceased_Country.y', 'New_Confirmed_Country.y',
## 'New_Total_Deceased_Country.y', 'Location.y', 'Population.y',
## 'Total_Mortality_Rate_Per_Capita.y', 'New_Mortality_Rate_Per_Capita.y',
## 'Total_Cases_Country_Per_Capita.y', 'rolling_average_confirmed.y',
## 'rolling_average_deceased.y', 'total_rolling_average_mortality.y',
## 'new_rolling_average_mortality.y' are duplicated in the result

Temporary_Data_Country <- merge(Temporary_Data_Country, Missing_Mean_New_Mortality, by = c("COUNTRY", "Date"), all=TRUE)

## Warning in merge.data.frame(Temporary_Data_Country,
## Missing_Mean_New_Mortality, : column names 'Total_Cases_Country.x',
## 'Total_Deceased_Country.x', 'New_Confirmed_Country.x',
## 'New_Total_Deceased_Country.x', 'Location.x', 'Population.x',
## 'Total_Mortality_Rate_Per_Capita.x', 'New_Mortality_Rate_Per_Capita.x',
## 'Total_Cases_Country_Per_Capita.x', 'rolling_average_confirmed.x',
## 'rolling_average_deceased.x', 'total_rolling_average_mortality.x',
## 'new_rolling_average_mortality.x', 'Total_Cases_Country.y',
## 'Total_Deceased_Country.y', 'New_Confirmed_Country.y',
## 'New_Total_Deceased_Country.y', 'Location.y', 'Population.y',
## 'Total_Mortality_Rate_Per_Capita.y', 'New_Mortality_Rate_Per_Capita.y',
## 'Total_Cases_Country_Per_Capita.y', 'rolling_average_confirmed.y',
## 'rolling_average_deceased.y', 'total_rolling_average_mortality.y',
## 'new_rolling_average_mortality.y' are duplicated in the result

# Lastly, we can replace the missing values in our temporary file (for the confirmed rolling averages prior to one week) with the newly calculated "partial" rolling averages. We then replace all missing values with zero (the only missing values which remain are for our first day, when naturally no new cases will be recorded, or, in the case of our rolling average for the mortality rate, for dates in which we don't yet have a recorded case).
Temporary_Data_Country$rolling_average_confirmed.x[is.na(Temporary_Data_Country$rolling_average_confirmed.x)] <- Temporary_Data_Country$rolling_average_confirmed2[is.na(Temporary_Data_Country$rolling_average_confirmed.x)]
Temporary_Data_Country$rolling_average_confirmed.x[is.na(Temporary_Data_Country$rolling_average_confirmed.x)] <- 0
Temporary_Data_Country$rolling_average_confirmed <- Temporary_Data_Country$rolling_average_confirmed.x
Temporary_Data_Country$rolling_average_deceased.x[is.na(Temporary_Data_Country$rolling_average_deceased.x)] <- Temporary_Data_Country$rolling_average_deceased2[is.na(Temporary_Data_Country$rolling_average_deceased.x)]
Temporary_Data_Country$rolling_average_deceased.x[is.na(Temporary_Data_Country$rolling_average_deceased.x)] <- 0
Temporary_Data_Country$rolling_average_deceased <- Temporary_Data_Country$rolling_average_deceased.x
Temporary_Data_Country$total_rolling_average_mortality.x[is.na(Temporary_Data_Country$total_rolling_average_mortality.x)] <- Temporary_Data_Country$total_rolling_average_mortality2[is.na(Temporary_Data_Country$total_rolling_average_mortality.x)]
Temporary_Data_Country$total_rolling_average_mortality.x[is.na(Temporary_Data_Country$total_rolling_average_mortality.x)] <- 0
Temporary_Data_Country$total_rolling_average_mortality <- Temporary_Data_Country$total_rolling_average_mortality.x
Temporary_Data_Country$new_rolling_average_mortality.x[is.na(Temporary_Data_Country$new_rolling_average_mortality.x)] <- Temporary_Data_Country$new_rolling_average_mortality2[is.na(Temporary_Data_Country$new_rolling_average_mortality.x)]
Temporary_Data_Country$new_rolling_average_mortality.x[is.na(Temporary_Data_Country$new_rolling_average_mortality.x)] <- 0
Temporary_Data_Country$new_rolling_average_mortality <- Temporary_Data_Country$new_rolling_average_mortality.x

# I only keep columns we care about (which are not repetitive).
Temporary_Data_Country$Total_Cases_Country <- Temporary_Data_Country$Total_Cases_Country.x
Temporary_Data_Country$Total_Deceased_Country <- Temporary_Data_Country$Total_Deceased_Country.x
Temporary_Data_Country$New_Confirmed_Country <- Temporary_Data_Country$New_Confirmed_Country.x
Temporary_Data_Country$New_Total_Deceased_Country <- Temporary_Data_Country$New_Total_Deceased_Country.x
Temporary_Data_Country$Death_Rate <- Temporary_Data_Country$Death_Rate.x
Temporary_Data_Country$Total_Mortality_Rate_Per_Capita <- Temporary_Data_Country$Total_Mortality_Rate_Per_Capita.x
Temporary_Data_Country$New_Mortality_Rate_Per_Capita <- Temporary_Data_Country$New_Mortality_Rate_Per_Capita.x
Temporary_Data_Country$Location <- Temporary_Data_Country$Location.x
Temporary_Data_Country$Population <- Temporary_Data_Country$Population.x
Temporary_Data_Country$Total_Cases_Country_Per_Capita <- Temporary_Data_Country$Total_Cases_Country_Per_Capita.x
Temporary_Data_Country$rolling_average_confirmed <- Temporary_Data_Country$rolling_average_confirmed.x
Temporary_Data_Country$rolling_average_deceased <- Temporary_Data_Country$rolling_average_deceased.x
Temporary_Data_Country$total_rolling_average_mortality <- Temporary_Data_Country$total_rolling_average_mortality.x
Temporary_Data_Country$new_rolling_average_mortality <- Temporary_Data_Country$new_rolling_average_mortality.x

colnames(Temporary_Data_Country)

##  [1] "COUNTRY"                           "Date"                             
##  [3] "Total_Cases_Country.x"             "Total_Deceased_Country.x"         
##  [5] "New_Confirmed_Country.x"           "New_Total_Deceased_Country.x"     
##  [7] "Location.x"                        "Population.x"                     
##  [9] "Total_Mortality_Rate_Per_Capita.x" "New_Mortality_Rate_Per_Capita.x"  
## [11] "Total_Cases_Country_Per_Capita.x"  "rolling_average_confirmed.x"      
## [13] "rolling_average_deceased.x"        "total_rolling_average_mortality.x"
## [15] "new_rolling_average_mortality.x"   "Total_Cases_Country.y"            
## [17] "Total_Deceased_Country.y"          "New_Confirmed_Country.y"          
## [19] "New_Total_Deceased_Country.y"      "Location.y"                       
## [21] "Population.y"                      "Total_Mortality_Rate_Per_Capita.y"
## [23] "New_Mortality_Rate_Per_Capita.y"   "Total_Cases_Country_Per_Capita.y" 
## [25] "rolling_average_confirmed.y"       "rolling_average_deceased.y"       
## [27] "total_rolling_average_mortality.y" "new_rolling_average_mortality.y"  
## [29] "rolling_average_confirmed2"        "Total_Cases_Country.x"            
## [31] "Total_Deceased_Country.x"          "New_Confirmed_Country.x"          
## [33] "New_Total_Deceased_Country.x"      "Location.x"                       
## [35] "Population.x"                      "Total_Mortality_Rate_Per_Capita.x"
## [37] "New_Mortality_Rate_Per_Capita.x"   "Total_Cases_Country_Per_Capita.x" 
## [39] "rolling_average_confirmed.x"       "rolling_average_deceased.x"       
## [41] "total_rolling_average_mortality.x" "new_rolling_average_mortality.x"  
## [43] "rolling_average_deceased2"         "Total_Cases_Country.y"            
## [45] "Total_Deceased_Country.y"          "New_Confirmed_Country.y"          
## [47] "New_Total_Deceased_Country.y"      "Location.y"                       
## [49] "Population.y"                      "Total_Mortality_Rate_Per_Capita.y"
## [51] "New_Mortality_Rate_Per_Capita.y"   "Total_Cases_Country_Per_Capita.y" 
## [53] "rolling_average_confirmed.y"       "rolling_average_deceased.y"       
## [55] "total_rolling_average_mortality.y" "new_rolling_average_mortality.y"  
## [57] "total_rolling_average_mortality2"  "Total_Cases_Country"              
## [59] "Total_Deceased_Country"            "New_Confirmed_Country"            
## [61] "New_Total_Deceased_Country"        "Location"                         
## [63] "Population"                        "Total_Mortality_Rate_Per_Capita"  
## [65] "New_Mortality_Rate_Per_Capita"     "Total_Cases_Country_Per_Capita"   
## [67] "rolling_average_confirmed"         "rolling_average_deceased"         
## [69] "total_rolling_average_mortality"   "new_rolling_average_mortality"    
## [71] "new_rolling_average_mortality2"

myvarstemp <- c("COUNTRY", "Date", "Total_Cases_Country", "Total_Deceased_Country", "New_Confirmed_Country", "New_Total_Deceased_Country", "Population", "Total_Mortality_Rate_Per_Capita", "New_Mortality_Rate_Per_Capita", "Total_Cases_Country_Per_Capita", "rolling_average_confirmed", "rolling_average_deceased", "total_rolling_average_mortality", "new_rolling_average_mortality")
Temporary_Data_Country <- Temporary_Data_Country[myvarstemp]

colnames(Temporary_Data_Country)

##  [1] "COUNTRY"                         "Date"                           
##  [3] "Total_Cases_Country"             "Total_Deceased_Country"         
##  [5] "New_Confirmed_Country"           "New_Total_Deceased_Country"     
##  [7] "Population"                      "Total_Mortality_Rate_Per_Capita"
##  [9] "New_Mortality_Rate_Per_Capita"   "Total_Cases_Country_Per_Capita" 
## [11] "rolling_average_confirmed"       "rolling_average_deceased"       
## [13] "total_rolling_average_mortality" "new_rolling_average_mortality"

Here, I add coordinates for each nation in the dataset.

# When we collapse the COVID data to the country level, we lose our coordinate data. I now merge a dataset on global coordinates by country from: https://developers.google.com/public-data/docs/canonical/countries_csv. Note that I edited country names to match the data in the CSV itself. 

Country_Coordinates <- read.csv("data/Country_Coordinates.csv")

Country_Coordinates$COUNTRY <- Country_Coordinates$Country

Country_Coordinates$latitude <- Country_Coordinates$Latitude 
Country_Coordinates$longitude <- Country_Coordinates$Longitude 

colnames(Country_Coordinates)

## [1] "Latitude"  "Longitude" "Country"   "COUNTRY"   "latitude"  "longitude"

myvarscoords <- c("COUNTRY", "latitude", "longitude")
Country_Coordinates <- Country_Coordinates[myvarscoords]

# Lastly, I merge the two dataframes.
Temporary_Data_Country <- merge(Temporary_Data_Country, Country_Coordinates, by = c("COUNTRY"), all=TRUE)

Labelling and saving our temporary data.

# I load the following two libraries in order to label our variables.
library(lattice)
library(Hmisc)

## Loading required package: survival

## Warning: package 'survival' was built under R version 3.6.2

## 
## Attaching package: 'survival'

## The following object is masked from 'package:caret':
## 
##     cluster

## Loading required package: Formula

## 
## Attaching package: 'Hmisc'

## The following object is masked from 'package:quantmod':
## 
##     Lag

## The following object is masked from 'package:psych':
## 
##     describe

## The following objects are masked from 'package:summarytools':
## 
##     label, label<-

## The following object is masked from 'package:plotly':
## 
##     subplot

## The following objects are masked from 'package:dplyr':
## 
##     src, summarize

## The following objects are masked from 'package:base':
## 
##     format.pval, units

label(Temporary_Data_Country$Total_Cases_Country) <- "Cumulative Sum of Confirmed Cases by Country (John Hopkins)"
label(Temporary_Data_Country$Total_Deceased_Country) <- "Cumulative Sum of Deaths by Country (John Hopkins)"
label(Temporary_Data_Country$New_Confirmed_Country) <- "Daily Increase in Confirmed Cases (John Hopkins)"
label(Temporary_Data_Country$New_Total_Deceased_Country) <- "Daily Increase in Deaths (John Hopkins)"
label(Temporary_Data_Country$Total_Mortality_Rate_Per_Capita) <- "Total Deaths by Population (Total_Deceased_Country/Population)"
label(Temporary_Data_Country$New_Mortality_Rate_Per_Capita) <- "New Deaths by Population (New_Total_Deceased_Country/Population)"
label(Temporary_Data_Country$Total_Cases_Country_Per_Capita) <- "Total Cases by Population (Total_Cases_Country/Population)"
label(Temporary_Data_Country$rolling_average_confirmed) <- "Seven Day Rolling Average of New Confirmed Cases by Country (with the exception of Days 1-7)"
label(Temporary_Data_Country$rolling_average_deceased) <- "Seven Day Rolling Average of New Deaths by Country (with the exception of Days 1-7)"
label(Temporary_Data_Country$total_rolling_average_mortality) <- "Seven Day Rolling Average of Total Case Mortality Rate by Country (with the exception of Days 1-7)"
label(Temporary_Data_Country$new_rolling_average_mortality) <- "Seven Day Rolling Average of New Case Mortality Rate by Country (with the exception of Days 1-7)"

# This represents the "Temporary_Data_Country.csv" file saved in the data folder. I recommend you load in the CSV directly to ensure there are no discrepancies between the edits made in the Cloud, as opposed to my own R Project. I do this below.

# Removing the Cloud version of the Temporary Data.
remove(Temporary_Data_Country)

# Importing the Temporary Data from the data folder. 
Temporary_Data_Country <- read_csv("data/Temporary_Data_Country.csv")

## Parsed with column specification:
## cols(
##   COUNTRY = col_character(),
##   Date = col_date(format = ""),
##   Total_Cases_Country = col_double(),
##   Total_Deceased_Country = col_double(),
##   New_Confirmed_Country = col_double(),
##   New_Total_Deceased_Country = col_double(),
##   Population = col_double(),
##   Total_Mortality_Rate_Per_Capita = col_double(),
##   New_Mortality_Rate_Per_Capita = col_double(),
##   Total_Cases_Country_Per_Capita = col_double(),
##   rolling_average_confirmed = col_double(),
##   rolling_average_deceased = col_double(),
##   total_rolling_average_mortality = col_double(),
##   new_rolling_average_mortality = col_double(),
##   latitude = col_double(),
##   longitude = col_double()
## )

Merging our government data and temporary data to create a final dataframe.

# Now, I want to merge our temporary data with the Oxford dataset on global policy responses. Please see a description of the variables here: https://www.bsg.ox.ac.uk/sites/default/files/2020-04/BSG-WP-2020-032-v5.0_0.pdf. 
# The source is: Hale, Thomas, Anna Petherick, Toby Phillips, Samuel Webster. “Variation in Government Responses to COVID-19” Version 5.0. Blavatnik School of Government Working Paper. April 28, 2020. Available: www.bsg.ox.ac.uk/covidtracker.
# See https://www.bsg.ox.ac.uk/sites/default/files/Calculation%20and%20presentation%20of%20the%20Stringency%20Index.pdf for information on how the stringency index was calculated.

# 
Government_Responses <- read_csv("data/Government_Responses.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   CountryName = col_character(),
##   CountryCode = col_character(),
##   C1_Flag = col_logical(),
##   C1_Notes = col_character(),
##   C2_Flag = col_logical(),
##   C2_Notes = col_character(),
##   C3_Flag = col_logical(),
##   C3_Notes = col_character(),
##   C4_Flag = col_logical(),
##   C4_Notes = col_logical(),
##   C5_Notes = col_character(),
##   C6_Flag = col_logical(),
##   C6_Notes = col_character(),
##   C7_Flag = col_logical(),
##   C7_Notes = col_character(),
##   C8_Notes = col_character(),
##   E1_Flag = col_logical(),
##   E1_Notes = col_logical(),
##   E2_Notes = col_logical(),
##   E3_Notes = col_character()
##   # ... with 8 more columns
## )

## See spec(...) for full column specifications.

## Warning: 230 parsing failures.
##  row      col           expected                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               actual                            file
## 3112 C4_Notes 1/0/T/F/TRUE/FALSE Events were closed in February and March but no explicit limits on numbers were set.
## All public events and gatherings through the upcoming weeks were now canceled, a major challenge with one of the most celebrated holidays of the year, Tsagaan Sar, or Mongolian Lunar New Year, quickly approaching in February. All of this pre-“social distancing.”
## 
## It was eventually announced that March public events would be canceled as well, including the official cancellation of the Ice Festival on Lake Khovsgol, the Winter Golden Eagle Festival, the camel festival in Umnugobi province and the Nauryz Festival nearer to the end of March. All of them major blows to the locals and the tourism dollars they rely on.
## https://web.archive.org/web/20200425111855/https://www.forbes.com/sites/breannawilson/2020/03/16/mongolia-announces-3-new-covid-19-cases-totaling-4-how-they-got-coronavirus-precautions-right/ 'data/Government_Responses.csv'
## 4558 C4_Notes 1/0/T/F/TRUE/FALSE It is not clear exactly when Wuhan started restricting gatherings. Needs more research.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              'data/Government_Responses.csv'
## 4827 M1_Notes 1/0/T/F/TRUE/FALSE Vietnam declares an epidemic
## https://web.archive.org/web/20200425135141/https://vietnaminsider.vn/vietnam-government-declares-novel-coronavirus-epidemic/                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         'data/Government_Responses.csv'
## 8073 C4_Notes 1/0/T/F/TRUE/FALSE https://web.archive.org/web/20200409225704/https://www.la7.it/nonelarena/video/lelenco-dei-comuni-in-quarantena-a-causa-del-coronavirus-23-02-2020-308963
## 
## Localised quarantine in Lombardy                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    'data/Government_Responses.csv'
## 8372 C4_Notes 1/0/T/F/TRUE/FALSE The Iraqi government bans 'all gatherings in public' of any size.  This applies to some gatherings labeled as private, such as funerals or weddings. https://www.reuters.com/article/us-china-health-iraq/iraq-bans-public-gatherings-on-coronavirus-fear-travel-ban-totals-nine-countries-idUSKCN20K2ZE                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             'data/Government_Responses.csv'
## .... ........ .................. .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... ...............................
## See problems(...) for more details.

# First, I convert the date to a proper format.
Government_Responses$Date <- ymd(Government_Responses$Date, quiet = FALSE, tz = NULL, locale = Sys.getlocale("LC_TIME"),
  truncated = 0)
order(Government_Responses$Date)

##     [1]     1     2     3     4     5     6     7     8     9    10    11    12
##    [13]    13    14    15    16    17    18    19    20    21    22    23    24
##    [25]    25    26    27    28    29    30    31    32    33    34    35    36
##    [37]    37    38    39    40    41    42    43    44    45    46    47    48
##    [49]    49    50    51    52    53    54    55    56    57    58    59    60
##    [61]    61    62    63    64    65    66    67    68    69    70    71    72
##    [73]    73    74    75    76    77    78    79    80    81    82    83    84
##    [85]    85    86    87    88    89    90    91    92    93    94    95    96
##    [97]    97    98    99   100   101   102   103   104   105   106   107   108
##   [109]   109   110   111   112   113   114   115   116   117   118   119   120
##   [121]   121   122   123   124   125   126   127   128   129   130   131   132
##   [133]   133   134   135   136   137   138   139   140   141   142   143   144
##   [145]   145   146   147   148   149   150   151   152   153   154   155   156
##   [157]   157   158   159   160   161   162   163   164   165   166   167   168
##   [169]   169   170   171   172   173   174   175   176   177   178   179   180
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##   [529]   529   530   531   532   533   534   535   536   537   538   539   540
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##   [733]   733   734   735   736   737   738   739   740   741   742   743   744
##   [745]   745   746   747   748   749   750   751   752   753   754   755   756
##   [757]   757   758   759   760   761   762   763   764   765   766   767   768
##   [769]   769   770   771   772   773   774   775   776   777   778   779   780
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##   [793]   793   794   795   796   797   798   799   800   801   802   803   804
##   [805]   805   806   807   808   809   810   811   812   813   814   815   816
##   [817]   817   818   819   820   821   822   823   824   825   826   827   828
##   [829]   829   830   831   832   833   834   835   836   837   838   839   840
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##   [937]   937   938   939   940   941   942   943   944   945   946   947   948
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##   [961]   961   962   963   964   965   966   967   968   969   970   971   972
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##  [1009]  1009  1010  1011  1012  1013  1014  1015  1016  1017  1018  1019  1020
##  [1021]  1021  1022  1023  1024  1025  1026  1027  1028  1029  1030  1031  1032
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##  [1117]  1117  1118  1119  1120  1121  1122  1123  1124  1125  1126  1127  1128
##  [1129]  1129  1130  1131  1132  1133  1134  1135  1136  1137  1138  1139  1140
##  [1141]  1141  1142  1143  1144  1145  1146  1147  1148  1149  1150  1151  1152
##  [1153]  1153  1154  1155  1156  1157  1158  1159  1160  1161  1162  1163  1164
##  [1165]  1165  1166  1167  1168  1169  1170  1171  1172  1173  1174  1175  1176
##  [1177]  1177  1178  1179  1180  1181  1182  1183  1184  1185  1186  1187  1188
##  [1189]  1189  1190  1191  1192  1193  1194  1195  1196  1197  1198  1199  1200
##  [1201]  1201  1202  1203  1204  1205  1206  1207  1208  1209  1210  1211  1212
##  [1213]  1213  1214  1215  1216  1217  1218  1219  1220  1221  1222  1223  1224
##  [1225]  1225  1226  1227  1228  1229  1230  1231  1232  1233  1234  1235  1236
##  [1237]  1237  1238  1239  1240  1241  1242  1243  1244  1245  1246  1247  1248
##  [1249]  1249  1250  1251  1252  1253  1254  1255  1256  1257  1258  1259  1260
##  [1261]  1261  1262  1263  1264  1265  1266  1267  1268  1269  1270  1271  1272
##  [1273]  1273  1274  1275  1276  1277  1278  1279  1280  1281  1282  1283  1284
##  [1285]  1285  1286  1287  1288  1289  1290  1291  1292  1293  1294  1295  1296
##  [1297]  1297  1298  1299  1300  1301  1302  1303  1304  1305  1306  1307  1308
##  [1309]  1309  1310  1311  1312  1313  1314  1315  1316  1317  1318  1319  1320
##  [1321]  1321  1322  1323  1324  1325  1326  1327  1328  1329  1330  1331  1332
##  [1333]  1333  1334  1335  1336  1337  1338  1339  1340  1341  1342  1343  1344
##  [1345]  1345  1346  1347  1348  1349  1350  1351  1352  1353  1354  1355  1356
##  [1357]  1357  1358  1359  1360  1361  1362  1363  1364  1365  1366  1367  1368
##  [1369]  1369  1370  1371  1372  1373  1374  1375  1376  1377  1378  1379  1380
##  [1381]  1381  1382  1383  1384  1385  1386  1387  1388  1389  1390  1391  1392
##  [1393]  1393  1394  1395  1396  1397  1398  1399  1400  1401  1402  1403  1404
##  [1405]  1405  1406  1407  1408  1409  1410  1411  1412  1413  1414  1415  1416
##  [1417]  1417  1418  1419  1420  1421  1422  1423  1424  1425  1426  1427  1428
##  [1429]  1429  1430  1431  1432  1433  1434  1435  1436  1437  1438  1439  1440
##  [1441]  1441  1442  1443  1444  1445  1446  1447  1448  1449  1450  1451  1452
##  [1453]  1453  1454  1455  1456  1457  1458  1459  1460  1461  1462  1463  1464
##  [1465]  1465  1466  1467  1468  1469  1470  1471  1472  1473  1474  1475  1476
##  [1477]  1477  1478  1479  1480  1481  1482  1483  1484  1485  1486  1487  1488
##  [1489]  1489  1490  1491  1492  1493  1494  1495  1496  1497  1498  1499  1500
##  [1501]  1501  1502  1503  1504  1505  1506  1507  1508  1509  1510  1511  1512
##  [1513]  1513  1514  1515  1516  1517  1518  1519  1520  1521  1522  1523  1524
##  [1525]  1525  1526  1527  1528  1529  1530  1531  1532  1533  1534  1535  1536
##  [1537]  1537  1538  1539  1540  1541  1542  1543  1544  1545  1546  1547  1548
##  [1549]  1549  1550  1551  1552  1553  1554  1555  1556  1557  1558  1559  1560
##  [1561]  1561  1562  1563  1564  1565  1566  1567  1568  1569  1570  1571  1572
##  [1573]  1573  1574  1575  1576  1577  1578  1579  1580  1581  1582  1583  1584
##  [1585]  1585  1586  1587  1588  1589  1590  1591  1592  1593  1594  1595  1596
##  [1597]  1597  1598  1599  1600  1601  1602  1603  1604  1605  1606  1607  1608
##  [1609]  1609  1610  1611  1612  1613  1614  1615  1616  1617  1618  1619  1620
##  [1621]  1621  1622  1623  1624  1625  1626  1627  1628  1629  1630  1631  1632
##  [1633]  1633  1634  1635  1636  1637  1638  1639  1640  1641  1642  1643  1644
##  [1645]  1645  1646  1647  1648  1649  1650  1651  1652  1653  1654  1655  1656
##  [1657]  1657  1658  1659  1660  1661  1662  1663  1664  1665  1666  1667  1668
##  [1669]  1669  1670  1671  1672  1673  1674  1675  1676  1677  1678  1679  1680
##  [1681]  1681  1682  1683  1684  1685  1686  1687  1688  1689  1690  1691  1692
##  [1693]  1693  1694  1695  1696  1697  1698  1699  1700  1701  1702  1703  1704
##  [1705]  1705  1706  1707  1708  1709  1710  1711  1712  1713  1714  1715  1716
##  [1717]  1717  1718  1719  1720  1721  1722  1723  1724  1725  1726  1727  1728
##  [1729]  1729  1730  1731  1732  1733  1734  1735  1736  1737  1738  1739  1740
##  [1741]  1741  1742  1743  1744  1745  1746  1747  1748  1749  1750  1751  1752
##  [1753]  1753  1754  1755  1756  1757  1758  1759  1760  1761  1762  1763  1764
##  [1765]  1765  1766  1767  1768  1769  1770  1771  1772  1773  1774  1775  1776
##  [1777]  1777  1778  1779  1780  1781  1782  1783  1784  1785  1786  1787  1788
##  [1789]  1789  1790  1791  1792  1793  1794  1795  1796  1797  1798  1799  1800
##  [1801]  1801  1802  1803  1804  1805  1806  1807  1808  1809  1810  1811  1812
##  [1813]  1813  1814  1815  1816  1817  1818  1819  1820  1821  1822  1823  1824
##  [1825]  1825  1826  1827  1828  1829  1830  1831  1832  1833  1834  1835  1836
##  [1837]  1837  1838  1839  1840  1841  1842  1843  1844  1845  1846  1847  1848
##  [1849]  1849  1850  1851  1852  1853  1854  1855  1856  1857  1858  1859  1860
##  [1861]  1861  1862  1863  1864  1865  1866  1867  1868  1869  1870  1871  1872
##  [1873]  1873  1874  1875  1876  1877  1878  1879  1880  1881  1882  1883  1884
##  [1885]  1885  1886  1887  1888  1889  1890  1891  1892  1893  1894  1895  1896
##  [1897]  1897  1898  1899  1900  1901  1902  1903  1904  1905  1906  1907  1908
##  [1909]  1909  1910  1911  1912  1913  1914  1915  1916  1917  1918  1919  1920
##  [1921]  1921  1922  1923  1924  1925  1926  1927  1928  1929  1930  1931  1932
##  [1933]  1933  1934  1935  1936  1937  1938  1939  1940  1941  1942  1943  1944
##  [1945]  1945  1946  1947  1948  1949  1950  1951  1952  1953  1954  1955  1956
##  [1957]  1957  1958  1959  1960  1961  1962  1963  1964  1965  1966  1967  1968
##  [1969]  1969  1970  1971  1972  1973  1974  1975  1976  1977  1978  1979  1980
##  [1981]  1981  1982  1983  1984  1985  1986  1987  1988  1989  1990  1991  1992
##  [1993]  1993  1994  1995  1996  1997  1998  1999  2000  2001  2002  2003  2004
##  [2005]  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  2016
##  [2017]  2017  2018  2019  2020  2021  2022  2023  2024  2025  2026  2027  2028
##  [2029]  2029  2030  2031  2032  2033  2034  2035  2036  2037  2038  2039  2040
##  [2041]  2041  2042  2043  2044  2045  2046  2047  2048  2049  2050  2051  2052
##  [2053]  2053  2054  2055  2056  2057  2058  2059  2060  2061  2062  2063  2064
##  [2065]  2065  2066  2067  2068  2069  2070  2071  2072  2073  2074  2075  2076
##  [2077]  2077  2078  2079  2080  2081  2082  2083  2084  2085  2086  2087  2088
##  [2089]  2089  2090  2091  2092  2093  2094  2095  2096  2097  2098  2099  2100
##  [2101]  2101  2102  2103  2104  2105  2106  2107  2108  2109  2110  2111  2112
##  [2113]  2113  2114  2115  2116  2117  2118  2119  2120  2121  2122  2123  2124
##  [2125]  2125  2126  2127  2128  2129  2130  2131  2132  2133  2134  2135  2136
##  [2137]  2137  2138  2139  2140  2141  2142  2143  2144  2145  2146  2147  2148
##  [2149]  2149  2150  2151  2152  2153  2154  2155  2156  2157  2158  2159  2160
##  [2161]  2161  2162  2163  2164  2165  2166  2167  2168  2169  2170  2171  2172
##  [2173]  2173  2174  2175  2176  2177  2178  2179  2180  2181  2182  2183  2184
##  [2185]  2185  2186  2187  2188  2189  2190  2191  2192  2193  2194  2195  2196
##  [2197]  2197  2198  2199  2200  2201  2202  2203  2204  2205  2206  2207  2208
##  [2209]  2209  2210  2211  2212  2213  2214  2215  2216  2217  2218  2219  2220
##  [2221]  2221  2222  2223  2224  2225  2226  2227  2228  2229  2230  2231  2232
##  [2233]  2233  2234  2235  2236  2237  2238  2239  2240  2241  2242  2243  2244
##  [2245]  2245  2246  2247  2248  2249  2250  2251  2252  2253  2254  2255  2256
##  [2257]  2257  2258  2259  2260  2261  2262  2263  2264  2265  2266  2267  2268
##  [2269]  2269  2270  2271  2272  2273  2274  2275  2276  2277  2278  2279  2280
##  [2281]  2281  2282  2283  2284  2285  2286  2287  2288  2289  2290  2291  2292
##  [2293]  2293  2294  2295  2296  2297  2298  2299  2300  2301  2302  2303  2304
##  [2305]  2305  2306  2307  2308  2309  2310  2311  2312  2313  2314  2315  2316
##  [2317]  2317  2318  2319  2320  2321  2322  2323  2324  2325  2326  2327  2328
##  [2329]  2329  2330  2331  2332  2333  2334  2335  2336  2337  2338  2339  2340
##  [2341]  2341  2342  2343  2344  2345  2346  2347  2348  2349  2350  2351  2352
##  [2353]  2353  2354  2355  2356  2357  2358  2359  2360  2361  2362  2363  2364
##  [2365]  2365  2366  2367  2368  2369  2370  2371  2372  2373  2374  2375  2376
##  [2377]  2377  2378  2379  2380  2381  2382  2383  2384  2385  2386  2387  2388
##  [2389]  2389  2390  2391  2392  2393  2394  2395  2396  2397  2398  2399  2400
##  [2401]  2401  2402  2403  2404  2405  2406  2407  2408  2409  2410  2411  2412
##  [2413]  2413  2414  2415  2416  2417  2418  2419  2420  2421  2422  2423  2424
##  [2425]  2425  2426  2427  2428  2429  2430  2431  2432  2433  2434  2435  2436
##  [2437]  2437  2438  2439  2440  2441  2442  2443  2444  2445  2446  2447  2448
##  [2449]  2449  2450  2451  2452  2453  2454  2455  2456  2457  2458  2459  2460
##  [2461]  2461  2462  2463  2464  2465  2466  2467  2468  2469  2470  2471  2472
##  [2473]  2473  2474  2475  2476  2477  2478  2479  2480  2481  2482  2483  2484
##  [2485]  2485  2486  2487  2488  2489  2490  2491  2492  2493  2494  2495  2496
##  [2497]  2497  2498  2499  2500  2501  2502  2503  2504  2505  2506  2507  2508
##  [2509]  2509  2510  2511  2512  2513  2514  2515  2516  2517  2518  2519  2520
##  [2521]  2521  2522  2523  2524  2525  2526  2527  2528  2529  2530  2531  2532
##  [2533]  2533  2534  2535  2536  2537  2538  2539  2540  2541  2542  2543  2544
##  [2545]  2545  2546  2547  2548  2549  2550  2551  2552  2553  2554  2555  2556
##  [2557]  2557  2558  2559  2560  2561  2562  2563  2564  2565  2566  2567  2568
##  [2569]  2569  2570  2571  2572  2573  2574  2575  2576  2577  2578  2579  2580
##  [2581]  2581  2582  2583  2584  2585  2586  2587  2588  2589  2590  2591  2592
##  [2593]  2593  2594  2595  2596  2597  2598  2599  2600  2601  2602  2603  2604
##  [2605]  2605  2606  2607  2608  2609  2610  2611  2612  2613  2614  2615  2616
##  [2617]  2617  2618  2619  2620  2621  2622  2623  2624  2625  2626  2627  2628
##  [2629]  2629  2630  2631  2632  2633  2634  2635  2636  2637  2638  2639  2640
##  [2641]  2641  2642  2643  2644  2645  2646  2647  2648  2649  2650  2651  2652
##  [2653]  2653  2654  2655  2656  2657  2658  2659  2660  2661  2662  2663  2664
##  [2665]  2665  2666  2667  2668  2669  2670  2671  2672  2673  2674  2675  2676
##  [2677]  2677  2678  2679  2680  2681  2682  2683  2684  2685  2686  2687  2688
##  [2689]  2689  2690  2691  2692  2693  2694  2695  2696  2697  2698  2699  2700
##  [2701]  2701  2702  2703  2704  2705  2706  2707  2708  2709  2710  2711  2712
##  [2713]  2713  2714  2715  2716  2717  2718  2719  2720  2721  2722  2723  2724
##  [2725]  2725  2726  2727  2728  2729  2730  2731  2732  2733  2734  2735  2736
##  [2737]  2737  2738  2739  2740  2741  2742  2743  2744  2745  2746  2747  2748
##  [2749]  2749  2750  2751  2752  2753  2754  2755  2756  2757  2758  2759  2760
##  [2761]  2761  2762  2763  2764  2765  2766  2767  2768  2769  2770  2771  2772
##  [2773]  2773  2774  2775  2776  2777  2778  2779  2780  2781  2782  2783  2784
##  [2785]  2785  2786  2787  2788  2789  2790  2791  2792  2793  2794  2795  2796
##  [2797]  2797  2798  2799  2800  2801  2802  2803  2804  2805  2806  2807  2808
##  [2809]  2809  2810  2811  2812  2813  2814  2815  2816  2817  2818  2819  2820
##  [2821]  2821  2822  2823  2824  2825  2826  2827  2828  2829  2830  2831  2832
##  [2833]  2833  2834  2835  2836  2837  2838  2839  2840  2841  2842  2843  2844
##  [2845]  2845  2846  2847  2848  2849  2850  2851  2852  2853  2854  2855  2856
##  [2857]  2857  2858  2859  2860  2861  2862  2863  2864  2865  2866  2867  2868
##  [2869]  2869  2870  2871  2872  2873  2874  2875  2876  2877  2878  2879  2880
##  [2881]  2881  2882  2883  2884  2885  2886  2887  2888  2889  2890  2891  2892
##  [2893]  2893  2894  2895  2896  2897  2898  2899  2900  2901  2902  2903  2904
##  [2905]  2905  2906  2907  2908  2909  2910  2911  2912  2913  2914  2915  2916
##  [2917]  2917  2918  2919  2920  2921  2922  2923  2924  2925  2926  2927  2928
##  [2929]  2929  2930  2931  2932  2933  2934  2935  2936  2937  2938  2939  2940
##  [2941]  2941  2942  2943  2944  2945  2946  2947  2948  2949  2950  2951  2952
##  [2953]  2953  2954  2955  2956  2957  2958  2959  2960  2961  2962  2963  2964
##  [2965]  2965  2966  2967  2968  2969  2970  2971  2972  2973  2974  2975  2976
##  [2977]  2977  2978  2979  2980  2981  2982  2983  2984  2985  2986  2987  2988
##  [2989]  2989  2990  2991  2992  2993  2994  2995  2996  2997  2998  2999  3000
##  [3001]  3001  3002  3003  3004  3005  3006  3007  3008  3009  3010  3011  3012
##  [3013]  3013  3014  3015  3016  3017  3018  3019  3020  3021  3022  3023  3024
##  [3025]  3025  3026  3027  3028  3029  3030  3031  3032  3033  3034  3035  3036
##  [3037]  3037  3038  3039  3040  3041  3042  3043  3044  3045  3046  3047  3048
##  [3049]  3049  3050  3051  3052  3053  3054  3055  3056  3057  3058  3059  3060
##  [3061]  3061  3062  3063  3064  3065  3066  3067  3068  3069  3070  3071  3072
##  [3073]  3073  3074  3075  3076  3077  3078  3079  3080  3081  3082  3083  3084
##  [3085]  3085  3086  3087  3088  3089  3090  3091  3092  3093  3094  3095  3096
##  [3097]  3097  3098  3099  3100  3101  3102  3103  3104  3105  3106  3107  3108
##  [3109]  3109  3110  3111  3112  3113  3114  3115  3116  3117  3118  3119  3120
##  [3121]  3121  3122  3123  3124  3125  3126  3127  3128  3129  3130  3131  3132
##  [3133]  3133  3134  3135  3136  3137  3138  3139  3140  3141  3142  3143  3144
##  [3145]  3145  3146  3147  3148  3149  3150  3151  3152  3153  3154  3155  3156
##  [3157]  3157  3158  3159  3160  3161  3162  3163  3164  3165  3166  3167  3168
##  [3169]  3169  3170  3171  3172  3173  3174  3175  3176  3177  3178  3179  3180
##  [3181]  3181  3182  3183  3184  3185  3186  3187  3188  3189  3190  3191  3192
##  [3193]  3193  3194  3195  3196  3197  3198  3199  3200  3201  3202  3203  3204
##  [3205]  3205  3206  3207  3208  3209  3210  3211  3212  3213  3214  3215  3216
##  [3217]  3217  3218  3219  3220  3221  3222  3223  3224  3225  3226  3227  3228
##  [3229]  3229  3230  3231  3232  3233  3234  3235  3236  3237  3238  3239  3240
##  [3241]  3241  3242  3243  3244  3245  3246  3247  3248  3249  3250  3251  3252
##  [3253]  3253  3254  3255  3256  3257  3258  3259  3260  3261  3262  3263  3264
##  [3265]  3265  3266  3267  3268  3269  3270  3271  3272  3273  3274  3275  3276
##  [3277]  3277  3278  3279  3280  3281  3282  3283  3284  3285  3286  3287  3288
##  [3289]  3289  3290  3291  3292  3293  3294  3295  3296  3297  3298  3299  3300
##  [3301]  3301  3302  3303  3304  3305  3306  3307  3308  3309  3310  3311  3312
##  [3313]  3313  3314  3315  3316  3317  3318  3319  3320  3321  3322  3323  3324
##  [3325]  3325  3326  3327  3328  3329  3330  3331  3332  3333  3334  3335  3336
##  [3337]  3337  3338  3339  3340  3341  3342  3343  3344  3345  3346  3347  3348
##  [3349]  3349  3350  3351  3352  3353  3354  3355  3356  3357  3358  3359  3360
##  [3361]  3361  3362  3363  3364  3365  3366  3367  3368  3369  3370  3371  3372
##  [3373]  3373  3374  3375  3376  3377  3378  3379  3380  3381  3382  3383  3384
##  [3385]  3385  3386  3387  3388  3389  3390  3391  3392  3393  3394  3395  3396
##  [3397]  3397  3398  3399  3400  3401  3402  3403  3404  3405  3406  3407  3408
##  [3409]  3409  3410  3411  3412  3413  3414  3415  3416  3417  3418  3419  3420
##  [3421]  3421  3422  3423  3424  3425  3426  3427  3428  3429  3430  3431  3432
##  [3433]  3433  3434  3435  3436  3437  3438  3439  3440  3441  3442  3443  3444
##  [3445]  3445  3446  3447  3448  3449  3450  3451  3452  3453  3454  3455  3456
##  [3457]  3457  3458  3459  3460  3461  3462  3463  3464  3465  3466  3467  3468
##  [3469]  3469  3470  3471  3472  3473  3474  3475  3476  3477  3478  3479  3480
##  [3481]  3481  3482  3483  3484  3485  3486  3487  3488  3489  3490  3491  3492
##  [3493]  3493  3494  3495  3496  3497  3498  3499  3500  3501  3502  3503  3504
##  [3505]  3505  3506  3507  3508  3509  3510  3511  3512  3513  3514  3515  3516
##  [3517]  3517  3518  3519  3520  3521  3522  3523  3524  3525  3526  3527  3528
##  [3529]  3529  3530  3531  3532  3533  3534  3535  3536  3537  3538  3539  3540
##  [3541]  3541  3542  3543  3544  3545  3546  3547  3548  3549  3550  3551  3552
##  [3553]  3553  3554  3555  3556  3557  3558  3559  3560  3561  3562  3563  3564
##  [3565]  3565  3566  3567  3568  3569  3570  3571  3572  3573  3574  3575  3576
##  [3577]  3577  3578  3579  3580  3581  3582  3583  3584  3585  3586  3587  3588
##  [3589]  3589  3590  3591  3592  3593  3594  3595  3596  3597  3598  3599  3600
##  [3601]  3601  3602  3603  3604  3605  3606  3607  3608  3609  3610  3611  3612
##  [3613]  3613  3614  3615  3616  3617  3618  3619  3620  3621  3622  3623  3624
##  [3625]  3625  3626  3627  3628  3629  3630  3631  3632  3633  3634  3635  3636
##  [3637]  3637  3638  3639  3640  3641  3642  3643  3644  3645  3646  3647  3648
##  [3649]  3649  3650  3651  3652  3653  3654  3655  3656  3657  3658  3659  3660
##  [3661]  3661  3662  3663  3664  3665  3666  3667  3668  3669  3670  3671  3672
##  [3673]  3673  3674  3675  3676  3677  3678  3679  3680  3681  3682  3683  3684
##  [3685]  3685  3686  3687  3688  3689  3690  3691  3692  3693  3694  3695  3696
##  [3697]  3697  3698  3699  3700  3701  3702  3703  3704  3705  3706  3707  3708
##  [3709]  3709  3710  3711  3712  3713  3714  3715  3716  3717  3718  3719  3720
##  [3721]  3721  3722  3723  3724  3725  3726  3727  3728  3729  3730  3731  3732
##  [3733]  3733  3734  3735  3736  3737  3738  3739  3740  3741  3742  3743  3744
##  [3745]  3745  3746  3747  3748  3749  3750  3751  3752  3753  3754  3755  3756
##  [3757]  3757  3758  3759  3760  3761  3762  3763  3764  3765  3766  3767  3768
##  [3769]  3769  3770  3771  3772  3773  3774  3775  3776  3777  3778  3779  3780
##  [3781]  3781  3782  3783  3784  3785  3786  3787  3788  3789  3790  3791  3792
##  [3793]  3793  3794  3795  3796  3797  3798  3799  3800  3801  3802  3803  3804
##  [3805]  3805  3806  3807  3808  3809  3810  3811  3812  3813  3814  3815  3816
##  [3817]  3817  3818  3819  3820  3821  3822  3823  3824  3825  3826  3827  3828
##  [3829]  3829  3830  3831  3832  3833  3834  3835  3836  3837  3838  3839  3840
##  [3841]  3841  3842  3843  3844  3845  3846  3847  3848  3849  3850  3851  3852
##  [3853]  3853  3854  3855  3856  3857  3858  3859  3860  3861  3862  3863  3864
##  [3865]  3865  3866  3867  3868  3869  3870  3871  3872  3873  3874  3875  3876
##  [3877]  3877  3878  3879  3880  3881  3882  3883  3884  3885  3886  3887  3888
##  [3889]  3889  3890  3891  3892  3893  3894  3895  3896  3897  3898  3899  3900
##  [3901]  3901  3902  3903  3904  3905  3906  3907  3908  3909  3910  3911  3912
##  [3913]  3913  3914  3915  3916  3917  3918  3919  3920  3921  3922  3923  3924
##  [3925]  3925  3926  3927  3928  3929  3930  3931  3932  3933  3934  3935  3936
##  [3937]  3937  3938  3939  3940  3941  3942  3943  3944  3945  3946  3947  3948
##  [3949]  3949  3950  3951  3952  3953  3954  3955  3956  3957  3958  3959  3960
##  [3961]  3961  3962  3963  3964  3965  3966  3967  3968  3969  3970  3971  3972
##  [3973]  3973  3974  3975  3976  3977  3978  3979  3980  3981  3982  3983  3984
##  [3985]  3985  3986  3987  3988  3989  3990  3991  3992  3993  3994  3995  3996
##  [3997]  3997  3998  3999  4000  4001  4002  4003  4004  4005  4006  4007  4008
##  [4009]  4009  4010  4011  4012  4013  4014  4015  4016  4017  4018  4019  4020
##  [4021]  4021  4022  4023  4024  4025  4026  4027  4028  4029  4030  4031  4032
##  [4033]  4033  4034  4035  4036  4037  4038  4039  4040  4041  4042  4043  4044
##  [4045]  4045  4046  4047  4048  4049  4050  4051  4052  4053  4054  4055  4056
##  [4057]  4057  4058  4059  4060  4061  4062  4063  4064  4065  4066  4067  4068
##  [4069]  4069  4070  4071  4072  4073  4074  4075  4076  4077  4078  4079  4080
##  [4081]  4081  4082  4083  4084  4085  4086  4087  4088  4089  4090  4091  4092
##  [4093]  4093  4094  4095  4096  4097  4098  4099  4100  4101  4102  4103  4104
##  [4105]  4105  4106  4107  4108  4109  4110  4111  4112  4113  4114  4115  4116
##  [4117]  4117  4118  4119  4120  4121  4122  4123  4124  4125  4126  4127  4128
##  [4129]  4129  4130  4131  4132  4133  4134  4135  4136  4137  4138  4139  4140
##  [4141]  4141  4142  4143  4144  4145  4146  4147  4148  4149  4150  4151  4152
##  [4153]  4153  4154  4155  4156  4157  4158  4159  4160  4161  4162  4163  4164
##  [4165]  4165  4166  4167  4168  4169  4170  4171  4172  4173  4174  4175  4176
##  [4177]  4177  4178  4179  4180  4181  4182  4183  4184  4185  4186  4187  4188
##  [4189]  4189  4190  4191  4192  4193  4194  4195  4196  4197  4198  4199  4200
##  [4201]  4201  4202  4203  4204  4205  4206  4207  4208  4209  4210  4211  4212
##  [4213]  4213  4214  4215  4216  4217  4218  4219  4220  4221  4222  4223  4224
##  [4225]  4225  4226  4227  4228  4229  4230  4231  4232  4233  4234  4235  4236
##  [4237]  4237  4238  4239  4240  4241  4242  4243  4244  4245  4246  4247  4248
##  [4249]  4249  4250  4251  4252  4253  4254  4255  4256  4257  4258  4259  4260
##  [4261]  4261  4262  4263  4264  4265  4266  4267  4268  4269  4270  4271  4272
##  [4273]  4273  4274  4275  4276  4277  4278  4279  4280  4281  4282  4283  4284
##  [4285]  4285  4286  4287  4288  4289  4290  4291  4292  4293  4294  4295  4296
##  [4297]  4297  4298  4299  4300  4301  4302  4303  4304  4305  4306  4307  4308
##  [4309]  4309  4310  4311  4312  4313  4314  4315  4316  4317  4318  4319  4320
##  [4321]  4321  4322  4323  4324  4325  4326  4327  4328  4329  4330  4331  4332
##  [4333]  4333  4334  4335  4336  4337  4338  4339  4340  4341  4342  4343  4344
##  [4345]  4345  4346  4347  4348  4349  4350  4351  4352  4353  4354  4355  4356
##  [4357]  4357  4358  4359  4360  4361  4362  4363  4364  4365  4366  4367  4368
##  [4369]  4369  4370  4371  4372  4373  4374  4375  4376  4377  4378  4379  4380
##  [4381]  4381  4382  4383  4384  4385  4386  4387  4388  4389  4390  4391  4392
##  [4393]  4393  4394  4395  4396  4397  4398  4399  4400  4401  4402  4403  4404
##  [4405]  4405  4406  4407  4408  4409  4410  4411  4412  4413  4414  4415  4416
##  [4417]  4417  4418  4419  4420  4421  4422  4423  4424  4425  4426  4427  4428
##  [4429]  4429  4430  4431  4432  4433  4434  4435  4436  4437  4438  4439  4440
##  [4441]  4441  4442  4443  4444  4445  4446  4447  4448  4449  4450  4451  4452
##  [4453]  4453  4454  4455  4456  4457  4458  4459  4460  4461  4462  4463  4464
##  [4465]  4465  4466  4467  4468  4469  4470  4471  4472  4473  4474  4475  4476
##  [4477]  4477  4478  4479  4480  4481  4482  4483  4484  4485  4486  4487  4488
##  [4489]  4489  4490  4491  4492  4493  4494  4495  4496  4497  4498  4499  4500
##  [4501]  4501  4502  4503  4504  4505  4506  4507  4508  4509  4510  4511  4512
##  [4513]  4513  4514  4515  4516  4517  4518  4519  4520  4521  4522  4523  4524
##  [4525]  4525  4526  4527  4528  4529  4530  4531  4532  4533  4534  4535  4536
##  [4537]  4537  4538  4539  4540  4541  4542  4543  4544  4545  4546  4547  4548
##  [4549]  4549  4550  4551  4552  4553  4554  4555  4556  4557  4558  4559  4560
##  [4561]  4561  4562  4563  4564  4565  4566  4567  4568  4569  4570  4571  4572
##  [4573]  4573  4574  4575  4576  4577  4578  4579  4580  4581  4582  4583  4584
##  [4585]  4585  4586  4587  4588  4589  4590  4591  4592  4593  4594  4595  4596
##  [4597]  4597  4598  4599  4600  4601  4602  4603  4604  4605  4606  4607  4608
##  [4609]  4609  4610  4611  4612  4613  4614  4615  4616  4617  4618  4619  4620
##  [4621]  4621  4622  4623  4624  4625  4626  4627  4628  4629  4630  4631  4632
##  [4633]  4633  4634  4635  4636  4637  4638  4639  4640  4641  4642  4643  4644
##  [4645]  4645  4646  4647  4648  4649  4650  4651  4652  4653  4654  4655  4656
##  [4657]  4657  4658  4659  4660  4661  4662  4663  4664  4665  4666  4667  4668
##  [4669]  4669  4670  4671  4672  4673  4674  4675  4676  4677  4678  4679  4680
##  [4681]  4681  4682  4683  4684  4685  4686  4687  4688  4689  4690  4691  4692
##  [4693]  4693  4694  4695  4696  4697  4698  4699  4700  4701  4702  4703  4704
##  [4705]  4705  4706  4707  4708  4709  4710  4711  4712  4713  4714  4715  4716
##  [4717]  4717  4718  4719  4720  4721  4722  4723  4724  4725  4726  4727  4728
##  [4729]  4729  4730  4731  4732  4733  4734  4735  4736  4737  4738  4739  4740
##  [4741]  4741  4742  4743  4744  4745  4746  4747  4748  4749  4750  4751  4752
##  [4753]  4753  4754  4755  4756  4757  4758  4759  4760  4761  4762  4763  4764
##  [4765]  4765  4766  4767  4768  4769  4770  4771  4772  4773  4774  4775  4776
##  [4777]  4777  4778  4779  4780  4781  4782  4783  4784  4785  4786  4787  4788
##  [4789]  4789  4790  4791  4792  4793  4794  4795  4796  4797  4798  4799  4800
##  [4801]  4801  4802  4803  4804  4805  4806  4807  4808  4809  4810  4811  4812
##  [4813]  4813  4814  4815  4816  4817  4818  4819  4820  4821  4822  4823  4824
##  [4825]  4825  4826  4827  4828  4829  4830  4831  4832  4833  4834  4835  4836
##  [4837]  4837  4838  4839  4840  4841  4842  4843  4844  4845  4846  4847  4848
##  [4849]  4849  4850  4851  4852  4853  4854  4855  4856  4857  4858  4859  4860
##  [4861]  4861  4862  4863  4864  4865  4866  4867  4868  4869  4870  4871  4872
##  [4873]  4873  4874  4875  4876  4877  4878  4879  4880  4881  4882  4883  4884
##  [4885]  4885  4886  4887  4888  4889  4890  4891  4892  4893  4894  4895  4896
##  [4897]  4897  4898  4899  4900  4901  4902  4903  4904  4905  4906  4907  4908
##  [4909]  4909  4910  4911  4912  4913  4914  4915  4916  4917  4918  4919  4920
##  [4921]  4921  4922  4923  4924  4925  4926  4927  4928  4929  4930  4931  4932
##  [4933]  4933  4934  4935  4936  4937  4938  4939  4940  4941  4942  4943  4944
##  [4945]  4945  4946  4947  4948  4949  4950  4951  4952  4953  4954  4955  4956
##  [4957]  4957  4958  4959  4960  4961  4962  4963  4964  4965  4966  4967  4968
##  [4969]  4969  4970  4971  4972  4973  4974  4975  4976  4977  4978  4979  4980
##  [4981]  4981  4982  4983  4984  4985  4986  4987  4988  4989  4990  4991  4992
##  [4993]  4993  4994  4995  4996  4997  4998  4999  5000  5001  5002  5003  5004
##  [5005]  5005  5006  5007  5008  5009  5010  5011  5012  5013  5014  5015  5016
##  [5017]  5017  5018  5019  5020  5021  5022  5023  5024  5025  5026  5027  5028
##  [5029]  5029  5030  5031  5032  5033  5034  5035  5036  5037  5038  5039  5040
##  [5041]  5041  5042  5043  5044  5045  5046  5047  5048  5049  5050  5051  5052
##  [5053]  5053  5054  5055  5056  5057  5058  5059  5060  5061  5062  5063  5064
##  [5065]  5065  5066  5067  5068  5069  5070  5071  5072  5073  5074  5075  5076
##  [5077]  5077  5078  5079  5080  5081  5082  5083  5084  5085  5086  5087  5088
##  [5089]  5089  5090  5091  5092  5093  5094  5095  5096  5097  5098  5099  5100
##  [5101]  5101  5102  5103  5104  5105  5106  5107  5108  5109  5110  5111  5112
##  [5113]  5113  5114  5115  5116  5117  5118  5119  5120  5121  5122  5123  5124
##  [5125]  5125  5126  5127  5128  5129  5130  5131  5132  5133  5134  5135  5136
##  [5137]  5137  5138  5139  5140  5141  5142  5143  5144  5145  5146  5147  5148
##  [5149]  5149  5150  5151  5152  5153  5154  5155  5156  5157  5158  5159  5160
##  [5161]  5161  5162  5163  5164  5165  5166  5167  5168  5169  5170  5171  5172
##  [5173]  5173  5174  5175  5176  5177  5178  5179  5180  5181  5182  5183  5184
##  [5185]  5185  5186  5187  5188  5189  5190  5191  5192  5193  5194  5195  5196
##  [5197]  5197  5198  5199  5200  5201  5202  5203  5204  5205  5206  5207  5208
##  [5209]  5209  5210  5211  5212  5213  5214  5215  5216  5217  5218  5219  5220
##  [5221]  5221  5222  5223  5224  5225  5226  5227  5228  5229  5230  5231  5232
##  [5233]  5233  5234  5235  5236  5237  5238  5239  5240  5241  5242  5243  5244
##  [5245]  5245  5246  5247  5248  5249  5250  5251  5252  5253  5254  5255  5256
##  [5257]  5257  5258  5259  5260  5261  5262  5263  5264  5265  5266  5267  5268
##  [5269]  5269  5270  5271  5272  5273  5274  5275  5276  5277  5278  5279  5280
##  [5281]  5281  5282  5283  5284  5285  5286  5287  5288  5289  5290  5291  5292
##  [5293]  5293  5294  5295  5296  5297  5298  5299  5300  5301  5302  5303  5304
##  [5305]  5305  5306  5307  5308  5309  5310  5311  5312  5313  5314  5315  5316
##  [5317]  5317  5318  5319  5320  5321  5322  5323  5324  5325  5326  5327  5328
##  [5329]  5329  5330  5331  5332  5333  5334  5335  5336  5337  5338  5339  5340
##  [5341]  5341  5342  5343  5344  5345  5346  5347  5348  5349  5350  5351  5352
##  [5353]  5353  5354  5355  5356  5357  5358  5359  5360  5361  5362  5363  5364
##  [5365]  5365  5366  5367  5368  5369  5370  5371  5372  5373  5374  5375  5376
##  [5377]  5377  5378  5379  5380  5381  5382  5383  5384  5385  5386  5387  5388
##  [5389]  5389  5390  5391  5392  5393  5394  5395  5396  5397  5398  5399  5400
##  [5401]  5401  5402  5403  5404  5405  5406  5407  5408  5409  5410  5411  5412
##  [5413]  5413  5414  5415  5416  5417  5418  5419  5420  5421  5422  5423  5424
##  [5425]  5425  5426  5427  5428  5429  5430  5431  5432  5433  5434  5435  5436
##  [5437]  5437  5438  5439  5440  5441  5442  5443  5444  5445  5446  5447  5448
##  [5449]  5449  5450  5451  5452  5453  5454  5455  5456  5457  5458  5459  5460
##  [5461]  5461  5462  5463  5464  5465  5466  5467  5468  5469  5470  5471  5472
##  [5473]  5473  5474  5475  5476  5477  5478  5479  5480  5481  5482  5483  5484
##  [5485]  5485  5486  5487  5488  5489  5490  5491  5492  5493  5494  5495  5496
##  [5497]  5497  5498  5499  5500  5501  5502  5503  5504  5505  5506  5507  5508
##  [5509]  5509  5510  5511  5512  5513  5514  5515  5516  5517  5518  5519  5520
##  [5521]  5521  5522  5523  5524  5525  5526  5527  5528  5529  5530  5531  5532
##  [5533]  5533  5534  5535  5536  5537  5538  5539  5540  5541  5542  5543  5544
##  [5545]  5545  5546  5547  5548  5549  5550  5551  5552  5553  5554  5555  5556
##  [5557]  5557  5558  5559  5560  5561  5562  5563  5564  5565  5566  5567  5568
##  [5569]  5569  5570  5571  5572  5573  5574  5575  5576  5577  5578  5579  5580
##  [5581]  5581  5582  5583  5584  5585  5586  5587  5588  5589  5590  5591  5592
##  [5593]  5593  5594  5595  5596  5597  5598  5599  5600  5601  5602  5603  5604
##  [5605]  5605  5606  5607  5608  5609  5610  5611  5612  5613  5614  5615  5616
##  [5617]  5617  5618  5619  5620  5621  5622  5623  5624  5625  5626  5627  5628
##  [5629]  5629  5630  5631  5632  5633  5634  5635  5636  5637  5638  5639  5640
##  [5641]  5641  5642  5643  5644  5645  5646  5647  5648  5649  5650  5651  5652
##  [5653]  5653  5654  5655  5656  5657  5658  5659  5660  5661  5662  5663  5664
##  [5665]  5665  5666  5667  5668  5669  5670  5671  5672  5673  5674  5675  5676
##  [5677]  5677  5678  5679  5680  5681  5682  5683  5684  5685  5686  5687  5688
##  [5689]  5689  5690  5691  5692  5693  5694  5695  5696  5697  5698  5699  5700
##  [5701]  5701  5702  5703  5704  5705  5706  5707  5708  5709  5710  5711  5712
##  [5713]  5713  5714  5715  5716  5717  5718  5719  5720  5721  5722  5723  5724
##  [5725]  5725  5726  5727  5728  5729  5730  5731  5732  5733  5734  5735  5736
##  [5737]  5737  5738  5739  5740  5741  5742  5743  5744  5745  5746  5747  5748
##  [5749]  5749  5750  5751  5752  5753  5754  5755  5756  5757  5758  5759  5760
##  [5761]  5761  5762  5763  5764  5765  5766  5767  5768  5769  5770  5771  5772
##  [5773]  5773  5774  5775  5776  5777  5778  5779  5780  5781  5782  5783  5784
##  [5785]  5785  5786  5787  5788  5789  5790  5791  5792  5793  5794  5795  5796
##  [5797]  5797  5798  5799  5800  5801  5802  5803  5804  5805  5806  5807  5808
##  [5809]  5809  5810  5811  5812  5813  5814  5815  5816  5817  5818  5819  5820
##  [5821]  5821  5822  5823  5824  5825  5826  5827  5828  5829  5830  5831  5832
##  [5833]  5833  5834  5835  5836  5837  5838  5839  5840  5841  5842  5843  5844
##  [5845]  5845  5846  5847  5848  5849  5850  5851  5852  5853  5854  5855  5856
##  [5857]  5857  5858  5859  5860  5861  5862  5863  5864  5865  5866  5867  5868
##  [5869]  5869  5870  5871  5872  5873  5874  5875  5876  5877  5878  5879  5880
##  [5881]  5881  5882  5883  5884  5885  5886  5887  5888  5889  5890  5891  5892
##  [5893]  5893  5894  5895  5896  5897  5898  5899  5900  5901  5902  5903  5904
##  [5905]  5905  5906  5907  5908  5909  5910  5911  5912  5913  5914  5915  5916
##  [5917]  5917  5918  5919  5920  5921  5922  5923  5924  5925  5926  5927  5928
##  [5929]  5929  5930  5931  5932  5933  5934  5935  5936  5937  5938  5939  5940
##  [5941]  5941  5942  5943  5944  5945  5946  5947  5948  5949  5950  5951  5952
##  [5953]  5953  5954  5955  5956  5957  5958  5959  5960  5961  5962  5963  5964
##  [5965]  5965  5966  5967  5968  5969  5970  5971  5972  5973  5974  5975  5976
##  [5977]  5977  5978  5979  5980  5981  5982  5983  5984  5985  5986  5987  5988
##  [5989]  5989  5990  5991  5992  5993  5994  5995  5996  5997  5998  5999  6000
##  [6001]  6001  6002  6003  6004  6005  6006  6007  6008  6009  6010  6011  6012
##  [6013]  6013  6014  6015  6016  6017  6018  6019  6020  6021  6022  6023  6024
##  [6025]  6025  6026  6027  6028  6029  6030  6031  6032  6033  6034  6035  6036
##  [6037]  6037  6038  6039  6040  6041  6042  6043  6044  6045  6046  6047  6048
##  [6049]  6049  6050  6051  6052  6053  6054  6055  6056  6057  6058  6059  6060
##  [6061]  6061  6062  6063  6064  6065  6066  6067  6068  6069  6070  6071  6072
##  [6073]  6073  6074  6075  6076  6077  6078  6079  6080  6081  6082  6083  6084
##  [6085]  6085  6086  6087  6088  6089  6090  6091  6092  6093  6094  6095  6096
##  [6097]  6097  6098  6099  6100  6101  6102  6103  6104  6105  6106  6107  6108
##  [6109]  6109  6110  6111  6112  6113  6114  6115  6116  6117  6118  6119  6120
##  [6121]  6121  6122  6123  6124  6125  6126  6127  6128  6129  6130  6131  6132
##  [6133]  6133  6134  6135  6136  6137  6138  6139  6140  6141  6142  6143  6144
##  [6145]  6145  6146  6147  6148  6149  6150  6151  6152  6153  6154  6155  6156
##  [6157]  6157  6158  6159  6160  6161  6162  6163  6164  6165  6166  6167  6168
##  [6169]  6169  6170  6171  6172  6173  6174  6175  6176  6177  6178  6179  6180
##  [6181]  6181  6182  6183  6184  6185  6186  6187  6188  6189  6190  6191  6192
##  [6193]  6193  6194  6195  6196  6197  6198  6199  6200  6201  6202  6203  6204
##  [6205]  6205  6206  6207  6208  6209  6210  6211  6212  6213  6214  6215  6216
##  [6217]  6217  6218  6219  6220  6221  6222  6223  6224  6225  6226  6227  6228
##  [6229]  6229  6230  6231  6232  6233  6234  6235  6236  6237  6238  6239  6240
##  [6241]  6241  6242  6243  6244  6245  6246  6247  6248  6249  6250  6251  6252
##  [6253]  6253  6254  6255  6256  6257  6258  6259  6260  6261  6262  6263  6264
##  [6265]  6265  6266  6267  6268  6269  6270  6271  6272  6273  6274  6275  6276
##  [6277]  6277  6278  6279  6280  6281  6282  6283  6284  6285  6286  6287  6288
##  [6289]  6289  6290  6291  6292  6293  6294  6295  6296  6297  6298  6299  6300
##  [6301]  6301  6302  6303  6304  6305  6306  6307  6308  6309  6310  6311  6312
##  [6313]  6313  6314  6315  6316  6317  6318  6319  6320  6321  6322  6323  6324
##  [6325]  6325  6326  6327  6328  6329  6330  6331  6332  6333  6334  6335  6336
##  [6337]  6337  6338  6339  6340  6341  6342  6343  6344  6345  6346  6347  6348
##  [6349]  6349  6350  6351  6352  6353  6354  6355  6356  6357  6358  6359  6360
##  [6361]  6361  6362  6363  6364  6365  6366  6367  6368  6369  6370  6371  6372
##  [6373]  6373  6374  6375  6376  6377  6378  6379  6380  6381  6382  6383  6384
##  [6385]  6385  6386  6387  6388  6389  6390  6391  6392  6393  6394  6395  6396
##  [6397]  6397  6398  6399  6400  6401  6402  6403  6404  6405  6406  6407  6408
##  [6409]  6409  6410  6411  6412  6413  6414  6415  6416  6417  6418  6419  6420
##  [6421]  6421  6422  6423  6424  6425  6426  6427  6428  6429  6430  6431  6432
##  [6433]  6433  6434  6435  6436  6437  6438  6439  6440  6441  6442  6443  6444
##  [6445]  6445  6446  6447  6448  6449  6450  6451  6452  6453  6454  6455  6456
##  [6457]  6457  6458  6459  6460  6461  6462  6463  6464  6465  6466  6467  6468
##  [6469]  6469  6470  6471  6472  6473  6474  6475  6476  6477  6478  6479  6480
##  [6481]  6481  6482  6483  6484  6485  6486  6487  6488  6489  6490  6491  6492
##  [6493]  6493  6494  6495  6496  6497  6498  6499  6500  6501  6502  6503  6504
##  [6505]  6505  6506  6507  6508  6509  6510  6511  6512  6513  6514  6515  6516
##  [6517]  6517  6518  6519  6520  6521  6522  6523  6524  6525  6526  6527  6528
##  [6529]  6529  6530  6531  6532  6533  6534  6535  6536  6537  6538  6539  6540
##  [6541]  6541  6542  6543  6544  6545  6546  6547  6548  6549  6550  6551  6552
##  [6553]  6553  6554  6555  6556  6557  6558  6559  6560  6561  6562  6563  6564
##  [6565]  6565  6566  6567  6568  6569  6570  6571  6572  6573  6574  6575  6576
##  [6577]  6577  6578  6579  6580  6581  6582  6583  6584  6585  6586  6587  6588
##  [6589]  6589  6590  6591  6592  6593  6594  6595  6596  6597  6598  6599  6600
##  [6601]  6601  6602  6603  6604  6605  6606  6607  6608  6609  6610  6611  6612
##  [6613]  6613  6614  6615  6616  6617  6618  6619  6620  6621  6622  6623  6624
##  [6625]  6625  6626  6627  6628  6629  6630  6631  6632  6633  6634  6635  6636
##  [6637]  6637  6638  6639  6640  6641  6642  6643  6644  6645  6646  6647  6648
##  [6649]  6649  6650  6651  6652  6653  6654  6655  6656  6657  6658  6659  6660
##  [6661]  6661  6662  6663  6664  6665  6666  6667  6668  6669  6670  6671  6672
##  [6673]  6673  6674  6675  6676  6677  6678  6679  6680  6681  6682  6683  6684
##  [6685]  6685  6686  6687  6688  6689  6690  6691  6692  6693  6694  6695  6696
##  [6697]  6697  6698  6699  6700  6701  6702  6703  6704  6705  6706  6707  6708
##  [6709]  6709  6710  6711  6712  6713  6714  6715  6716  6717  6718  6719  6720
##  [6721]  6721  6722  6723  6724  6725  6726  6727  6728  6729  6730  6731  6732
##  [6733]  6733  6734  6735  6736  6737  6738  6739  6740  6741  6742  6743  6744
##  [6745]  6745  6746  6747  6748  6749  6750  6751  6752  6753  6754  6755  6756
##  [6757]  6757  6758  6759  6760  6761  6762  6763  6764  6765  6766  6767  6768
##  [6769]  6769  6770  6771  6772  6773  6774  6775  6776  6777  6778  6779  6780
##  [6781]  6781  6782  6783  6784  6785  6786  6787  6788  6789  6790  6791  6792
##  [6793]  6793  6794  6795  6796  6797  6798  6799  6800  6801  6802  6803  6804
##  [6805]  6805  6806  6807  6808  6809  6810  6811  6812  6813  6814  6815  6816
##  [6817]  6817  6818  6819  6820  6821  6822  6823  6824  6825  6826  6827  6828
##  [6829]  6829  6830  6831  6832  6833  6834  6835  6836  6837  6838  6839  6840
##  [6841]  6841  6842  6843  6844  6845  6846  6847  6848  6849  6850  6851  6852
##  [6853]  6853  6854  6855  6856  6857  6858  6859  6860  6861  6862  6863  6864
##  [6865]  6865  6866  6867  6868  6869  6870  6871  6872  6873  6874  6875  6876
##  [6877]  6877  6878  6879  6880  6881  6882  6883  6884  6885  6886  6887  6888
##  [6889]  6889  6890  6891  6892  6893  6894  6895  6896  6897  6898  6899  6900
##  [6901]  6901  6902  6903  6904  6905  6906  6907  6908  6909  6910  6911  6912
##  [6913]  6913  6914  6915  6916  6917  6918  6919  6920  6921  6922  6923  6924
##  [6925]  6925  6926  6927  6928  6929  6930  6931  6932  6933  6934  6935  6936
##  [6937]  6937  6938  6939  6940  6941  6942  6943  6944  6945  6946  6947  6948
##  [6949]  6949  6950  6951  6952  6953  6954  6955  6956  6957  6958  6959  6960
##  [6961]  6961  6962  6963  6964  6965  6966  6967  6968  6969  6970  6971  6972
##  [6973]  6973  6974  6975  6976  6977  6978  6979  6980  6981  6982  6983  6984
##  [6985]  6985  6986  6987  6988  6989  6990  6991  6992  6993  6994  6995  6996
##  [6997]  6997  6998  6999  7000  7001  7002  7003  7004  7005  7006  7007  7008
##  [7009]  7009  7010  7011  7012  7013  7014  7015  7016  7017  7018  7019  7020
##  [7021]  7021  7022  7023  7024  7025  7026  7027  7028  7029  7030  7031  7032
##  [7033]  7033  7034  7035  7036  7037  7038  7039  7040  7041  7042  7043  7044
##  [7045]  7045  7046  7047  7048  7049  7050  7051  7052  7053  7054  7055  7056
##  [7057]  7057  7058  7059  7060  7061  7062  7063  7064  7065  7066  7067  7068
##  [7069]  7069  7070  7071  7072  7073  7074  7075  7076  7077  7078  7079  7080
##  [7081]  7081  7082  7083  7084  7085  7086  7087  7088  7089  7090  7091  7092
##  [7093]  7093  7094  7095  7096  7097  7098  7099  7100  7101  7102  7103  7104
##  [7105]  7105  7106  7107  7108  7109  7110  7111  7112  7113  7114  7115  7116
##  [7117]  7117  7118  7119  7120  7121  7122  7123  7124  7125  7126  7127  7128
##  [7129]  7129  7130  7131  7132  7133  7134  7135  7136  7137  7138  7139  7140
##  [7141]  7141  7142  7143  7144  7145  7146  7147  7148  7149  7150  7151  7152
##  [7153]  7153  7154  7155  7156  7157  7158  7159  7160  7161  7162  7163  7164
##  [7165]  7165  7166  7167  7168  7169  7170  7171  7172  7173  7174  7175  7176
##  [7177]  7177  7178  7179  7180  7181  7182  7183  7184  7185  7186  7187  7188
##  [7189]  7189  7190  7191  7192  7193  7194  7195  7196  7197  7198  7199  7200
##  [7201]  7201  7202  7203  7204  7205  7206  7207  7208  7209  7210  7211  7212
##  [7213]  7213  7214  7215  7216  7217  7218  7219  7220  7221  7222  7223  7224
##  [7225]  7225  7226  7227  7228  7229  7230  7231  7232  7233  7234  7235  7236
##  [7237]  7237  7238  7239  7240  7241  7242  7243  7244  7245  7246  7247  7248
##  [7249]  7249  7250  7251  7252  7253  7254  7255  7256  7257  7258  7259  7260
##  [7261]  7261  7262  7263  7264  7265  7266  7267  7268  7269  7270  7271  7272
##  [7273]  7273  7274  7275  7276  7277  7278  7279  7280  7281  7282  7283  7284
##  [7285]  7285  7286  7287  7288  7289  7290  7291  7292  7293  7294  7295  7296
##  [7297]  7297  7298  7299  7300  7301  7302  7303  7304  7305  7306  7307  7308
##  [7309]  7309  7310  7311  7312  7313  7314  7315  7316  7317  7318  7319  7320
##  [7321]  7321  7322  7323  7324  7325  7326  7327  7328  7329  7330  7331  7332
##  [7333]  7333  7334  7335  7336  7337  7338  7339  7340  7341  7342  7343  7344
##  [7345]  7345  7346  7347  7348  7349  7350  7351  7352  7353  7354  7355  7356
##  [7357]  7357  7358  7359  7360  7361  7362  7363  7364  7365  7366  7367  7368
##  [7369]  7369  7370  7371  7372  7373  7374  7375  7376  7377  7378  7379  7380
##  [7381]  7381  7382  7383  7384  7385  7386  7387  7388  7389  7390  7391  7392
##  [7393]  7393  7394  7395  7396  7397  7398  7399  7400  7401  7402  7403  7404
##  [7405]  7405  7406  7407  7408  7409  7410  7411  7412  7413  7414  7415  7416
##  [7417]  7417  7418  7419  7420  7421  7422  7423  7424  7425  7426  7427  7428
##  [7429]  7429  7430  7431  7432  7433  7434  7435  7436  7437  7438  7439  7440
##  [7441]  7441  7442  7443  7444  7445  7446  7447  7448  7449  7450  7451  7452
##  [7453]  7453  7454  7455  7456  7457  7458  7459  7460  7461  7462  7463  7464
##  [7465]  7465  7466  7467  7468  7469  7470  7471  7472  7473  7474  7475  7476
##  [7477]  7477  7478  7479  7480  7481  7482  7483  7484  7485  7486  7487  7488
##  [7489]  7489  7490  7491  7492  7493  7494  7495  7496  7497  7498  7499  7500
##  [7501]  7501  7502  7503  7504  7505  7506  7507  7508  7509  7510  7511  7512
##  [7513]  7513  7514  7515  7516  7517  7518  7519  7520  7521  7522  7523  7524
##  [7525]  7525  7526  7527  7528  7529  7530  7531  7532  7533  7534  7535  7536
##  [7537]  7537  7538  7539  7540  7541  7542  7543  7544  7545  7546  7547  7548
##  [7549]  7549  7550  7551  7552  7553  7554  7555  7556  7557  7558  7559  7560
##  [7561]  7561  7562  7563  7564  7565  7566  7567  7568  7569  7570  7571  7572
##  [7573]  7573  7574  7575  7576  7577  7578  7579  7580  7581  7582  7583  7584
##  [7585]  7585  7586  7587  7588  7589  7590  7591  7592  7593  7594  7595  7596
##  [7597]  7597  7598  7599  7600  7601  7602  7603  7604  7605  7606  7607  7608
##  [7609]  7609  7610  7611  7612  7613  7614  7615  7616  7617  7618  7619  7620
##  [7621]  7621  7622  7623  7624  7625  7626  7627  7628  7629  7630  7631  7632
##  [7633]  7633  7634  7635  7636  7637  7638  7639  7640  7641  7642  7643  7644
##  [7645]  7645  7646  7647  7648  7649  7650  7651  7652  7653  7654  7655  7656
##  [7657]  7657  7658  7659  7660  7661  7662  7663  7664  7665  7666  7667  7668
##  [7669]  7669  7670  7671  7672  7673  7674  7675  7676  7677  7678  7679  7680
##  [7681]  7681  7682  7683  7684  7685  7686  7687  7688  7689  7690  7691  7692
##  [7693]  7693  7694  7695  7696  7697  7698  7699  7700  7701  7702  7703  7704
##  [7705]  7705  7706  7707  7708  7709  7710  7711  7712  7713  7714  7715  7716
##  [7717]  7717  7718  7719  7720  7721  7722  7723  7724  7725  7726  7727  7728
##  [7729]  7729  7730  7731  7732  7733  7734  7735  7736  7737  7738  7739  7740
##  [7741]  7741  7742  7743  7744  7745  7746  7747  7748  7749  7750  7751  7752
##  [7753]  7753  7754  7755  7756  7757  7758  7759  7760  7761  7762  7763  7764
##  [7765]  7765  7766  7767  7768  7769  7770  7771  7772  7773  7774  7775  7776
##  [7777]  7777  7778  7779  7780  7781  7782  7783  7784  7785  7786  7787  7788
##  [7789]  7789  7790  7791  7792  7793  7794  7795  7796  7797  7798  7799  7800
##  [7801]  7801  7802  7803  7804  7805  7806  7807  7808  7809  7810  7811  7812
##  [7813]  7813  7814  7815  7816  7817  7818  7819  7820  7821  7822  7823  7824
##  [7825]  7825  7826  7827  7828  7829  7830  7831  7832  7833  7834  7835  7836
##  [7837]  7837  7838  7839  7840  7841  7842  7843  7844  7845  7846  7847  7848
##  [7849]  7849  7850  7851  7852  7853  7854  7855  7856  7857  7858  7859  7860
##  [7861]  7861  7862  7863  7864  7865  7866  7867  7868  7869  7870  7871  7872
##  [7873]  7873  7874  7875  7876  7877  7878  7879  7880  7881  7882  7883  7884
##  [7885]  7885  7886  7887  7888  7889  7890  7891  7892  7893  7894  7895  7896
##  [7897]  7897  7898  7899  7900  7901  7902  7903  7904  7905  7906  7907  7908
##  [7909]  7909  7910  7911  7912  7913  7914  7915  7916  7917  7918  7919  7920
##  [7921]  7921  7922  7923  7924  7925  7926  7927  7928  7929  7930  7931  7932
##  [7933]  7933  7934  7935  7936  7937  7938  7939  7940  7941  7942  7943  7944
##  [7945]  7945  7946  7947  7948  7949  7950  7951  7952  7953  7954  7955  7956
##  [7957]  7957  7958  7959  7960  7961  7962  7963  7964  7965  7966  7967  7968
##  [7969]  7969  7970  7971  7972  7973  7974  7975  7976  7977  7978  7979  7980
##  [7981]  7981  7982  7983  7984  7985  7986  7987  7988  7989  7990  7991  7992
##  [7993]  7993  7994  7995  7996  7997  7998  7999  8000  8001  8002  8003  8004
##  [8005]  8005  8006  8007  8008  8009  8010  8011  8012  8013  8014  8015  8016
##  [8017]  8017  8018  8019  8020  8021  8022  8023  8024  8025  8026  8027  8028
##  [8029]  8029  8030  8031  8032  8033  8034  8035  8036  8037  8038  8039  8040
##  [8041]  8041  8042  8043  8044  8045  8046  8047  8048  8049  8050  8051  8052
##  [8053]  8053  8054  8055  8056  8057  8058  8059  8060  8061  8062  8063  8064
##  [8065]  8065  8066  8067  8068  8069  8070  8071  8072  8073  8074  8075  8076
##  [8077]  8077  8078  8079  8080  8081  8082  8083  8084  8085  8086  8087  8088
##  [8089]  8089  8090  8091  8092  8093  8094  8095  8096  8097  8098  8099  8100
##  [8101]  8101  8102  8103  8104  8105  8106  8107  8108  8109  8110  8111  8112
##  [8113]  8113  8114  8115  8116  8117  8118  8119  8120  8121  8122  8123  8124
##  [8125]  8125  8126  8127  8128  8129  8130  8131  8132  8133  8134  8135  8136
##  [8137]  8137  8138  8139  8140  8141  8142  8143  8144  8145  8146  8147  8148
##  [8149]  8149  8150  8151  8152  8153  8154  8155  8156  8157  8158  8159  8160
##  [8161]  8161  8162  8163  8164  8165  8166  8167  8168  8169  8170  8171  8172
##  [8173]  8173  8174  8175  8176  8177  8178  8179  8180  8181  8182  8183  8184
##  [8185]  8185  8186  8187  8188  8189  8190  8191  8192  8193  8194  8195  8196
##  [8197]  8197  8198  8199  8200  8201  8202  8203  8204  8205  8206  8207  8208
##  [8209]  8209  8210  8211  8212  8213  8214  8215  8216  8217  8218  8219  8220
##  [8221]  8221  8222  8223  8224  8225  8226  8227  8228  8229  8230  8231  8232
##  [8233]  8233  8234  8235  8236  8237  8238  8239  8240  8241  8242  8243  8244
##  [8245]  8245  8246  8247  8248  8249  8250  8251  8252  8253  8254  8255  8256
##  [8257]  8257  8258  8259  8260  8261  8262  8263  8264  8265  8266  8267  8268
##  [8269]  8269  8270  8271  8272  8273  8274  8275  8276  8277  8278  8279  8280
##  [8281]  8281  8282  8283  8284  8285  8286  8287  8288  8289  8290  8291  8292
##  [8293]  8293  8294  8295  8296  8297  8298  8299  8300  8301  8302  8303  8304
##  [8305]  8305  8306  8307  8308  8309  8310  8311  8312  8313  8314  8315  8316
##  [8317]  8317  8318  8319  8320  8321  8322  8323  8324  8325  8326  8327  8328
##  [8329]  8329  8330  8331  8332  8333  8334  8335  8336  8337  8338  8339  8340
##  [8341]  8341  8342  8343  8344  8345  8346  8347  8348  8349  8350  8351  8352
##  [8353]  8353  8354  8355  8356  8357  8358  8359  8360  8361  8362  8363  8364
##  [8365]  8365  8366  8367  8368  8369  8370  8371  8372  8373  8374  8375  8376
##  [8377]  8377  8378  8379  8380  8381  8382  8383  8384  8385  8386  8387  8388
##  [8389]  8389  8390  8391  8392  8393  8394  8395  8396  8397  8398  8399  8400
##  [8401]  8401  8402  8403  8404  8405  8406  8407  8408  8409  8410  8411  8412
##  [8413]  8413  8414  8415  8416  8417  8418  8419  8420  8421  8422  8423  8424
##  [8425]  8425  8426  8427  8428  8429  8430  8431  8432  8433  8434  8435  8436
##  [8437]  8437  8438  8439  8440  8441  8442  8443  8444  8445  8446  8447  8448
##  [8449]  8449  8450  8451  8452  8453  8454  8455  8456  8457  8458  8459  8460
##  [8461]  8461  8462  8463  8464  8465  8466  8467  8468  8469  8470  8471  8472
##  [8473]  8473  8474  8475  8476  8477  8478  8479  8480  8481  8482  8483  8484
##  [8485]  8485  8486  8487  8488  8489  8490  8491  8492  8493  8494  8495  8496
##  [8497]  8497  8498  8499  8500  8501  8502  8503  8504  8505  8506  8507  8508
##  [8509]  8509  8510  8511  8512  8513  8514  8515  8516  8517  8518  8519  8520
##  [8521]  8521  8522  8523  8524  8525  8526  8527  8528  8529  8530  8531  8532
##  [8533]  8533  8534  8535  8536  8537  8538  8539  8540  8541  8542  8543  8544
##  [8545]  8545  8546  8547  8548  8549  8550  8551  8552  8553  8554  8555  8556
##  [8557]  8557  8558  8559  8560  8561  8562  8563  8564  8565  8566  8567  8568
##  [8569]  8569  8570  8571  8572  8573  8574  8575  8576  8577  8578  8579  8580
##  [8581]  8581  8582  8583  8584  8585  8586  8587  8588  8589  8590  8591  8592
##  [8593]  8593  8594  8595  8596  8597  8598  8599  8600  8601  8602  8603  8604
##  [8605]  8605  8606  8607  8608  8609  8610  8611  8612  8613  8614  8615  8616
##  [8617]  8617  8618  8619  8620  8621  8622  8623  8624  8625  8626  8627  8628
##  [8629]  8629  8630  8631  8632  8633  8634  8635  8636  8637  8638  8639  8640
##  [8641]  8641  8642  8643  8644  8645  8646  8647  8648  8649  8650  8651  8652
##  [8653]  8653  8654  8655  8656  8657  8658  8659  8660  8661  8662  8663  8664
##  [8665]  8665  8666  8667  8668  8669  8670  8671  8672  8673  8674  8675  8676
##  [8677]  8677  8678  8679  8680  8681  8682  8683  8684  8685  8686  8687  8688
##  [8689]  8689  8690  8691  8692  8693  8694  8695  8696  8697  8698  8699  8700
##  [8701]  8701  8702  8703  8704  8705  8706  8707  8708  8709  8710  8711  8712
##  [8713]  8713  8714  8715  8716  8717  8718  8719  8720  8721  8722  8723  8724
##  [8725]  8725  8726  8727  8728  8729  8730  8731  8732  8733  8734  8735  8736
##  [8737]  8737  8738  8739  8740  8741  8742  8743  8744  8745  8746  8747  8748
##  [8749]  8749  8750  8751  8752  8753  8754  8755  8756  8757  8758  8759  8760
##  [8761]  8761  8762  8763  8764  8765  8766  8767  8768  8769  8770  8771  8772
##  [8773]  8773  8774  8775  8776  8777  8778  8779  8780  8781  8782  8783  8784
##  [8785]  8785  8786  8787  8788  8789  8790  8791  8792  8793  8794  8795  8796
##  [8797]  8797  8798  8799  8800  8801  8802  8803  8804  8805  8806  8807  8808
##  [8809]  8809  8810  8811  8812  8813  8814  8815  8816  8817  8818  8819  8820
##  [8821]  8821  8822  8823  8824  8825  8826  8827  8828  8829  8830  8831  8832
##  [8833]  8833  8834  8835  8836  8837  8838  8839  8840  8841  8842  8843  8844
##  [8845]  8845  8846  8847  8848  8849  8850  8851  8852  8853  8854  8855  8856
##  [8857]  8857  8858  8859  8860  8861  8862  8863  8864  8865  8866  8867  8868
##  [8869]  8869  8870  8871  8872  8873  8874  8875  8876  8877  8878  8879  8880
##  [8881]  8881  8882  8883  8884  8885  8886  8887  8888  8889  8890  8891  8892
##  [8893]  8893  8894  8895  8896  8897  8898  8899  8900  8901  8902  8903  8904
##  [8905]  8905  8906  8907  8908  8909  8910  8911  8912  8913  8914  8915  8916
##  [8917]  8917  8918  8919  8920  8921  8922  8923  8924  8925  8926  8927  8928
##  [8929]  8929  8930  8931  8932  8933  8934  8935  8936  8937  8938  8939  8940
##  [8941]  8941  8942  8943  8944  8945  8946  8947  8948  8949  8950  8951  8952
##  [8953]  8953  8954  8955  8956  8957  8958  8959  8960  8961  8962  8963  8964
##  [8965]  8965  8966  8967  8968  8969  8970  8971  8972  8973  8974  8975  8976
##  [8977]  8977  8978  8979  8980  8981  8982  8983  8984  8985  8986  8987  8988
##  [8989]  8989  8990  8991  8992  8993  8994  8995  8996  8997  8998  8999  9000
##  [9001]  9001  9002  9003  9004  9005  9006  9007  9008  9009  9010  9011  9012
##  [9013]  9013  9014  9015  9016  9017  9018  9019  9020  9021  9022  9023  9024
##  [9025]  9025  9026  9027  9028  9029  9030  9031  9032  9033  9034  9035  9036
##  [9037]  9037  9038  9039  9040  9041  9042  9043  9044  9045  9046  9047  9048
##  [9049]  9049  9050  9051  9052  9053  9054  9055  9056  9057  9058  9059  9060
##  [9061]  9061  9062  9063  9064  9065  9066  9067  9068  9069  9070  9071  9072
##  [9073]  9073  9074  9075  9076  9077  9078  9079  9080  9081  9082  9083  9084
##  [9085]  9085  9086  9087  9088  9089  9090  9091  9092  9093  9094  9095  9096
##  [9097]  9097  9098  9099  9100  9101  9102  9103  9104  9105  9106  9107  9108
##  [9109]  9109  9110  9111  9112  9113  9114  9115  9116  9117  9118  9119  9120
##  [9121]  9121  9122  9123  9124  9125  9126  9127  9128  9129  9130  9131  9132
##  [9133]  9133  9134  9135  9136  9137  9138  9139  9140  9141  9142  9143  9144
##  [9145]  9145  9146  9147  9148  9149  9150  9151  9152  9153  9154  9155  9156
##  [9157]  9157  9158  9159  9160  9161  9162  9163  9164  9165  9166  9167  9168
##  [9169]  9169  9170  9171  9172  9173  9174  9175  9176  9177  9178  9179  9180
##  [9181]  9181  9182  9183  9184  9185  9186  9187  9188  9189  9190  9191  9192
##  [9193]  9193  9194  9195  9196  9197  9198  9199  9200  9201  9202  9203  9204
##  [9205]  9205  9206  9207  9208  9209  9210  9211  9212  9213  9214  9215  9216
##  [9217]  9217  9218  9219  9220  9221  9222  9223  9224  9225  9226  9227  9228
##  [9229]  9229  9230  9231  9232  9233  9234  9235  9236  9237  9238  9239  9240
##  [9241]  9241  9242  9243  9244  9245  9246  9247  9248  9249  9250  9251  9252
##  [9253]  9253  9254  9255  9256  9257  9258  9259  9260  9261  9262  9263  9264
##  [9265]  9265  9266  9267  9268  9269  9270  9271  9272  9273  9274  9275  9276
##  [9277]  9277  9278  9279  9280  9281  9282  9283  9284  9285  9286  9287  9288
##  [9289]  9289  9290  9291  9292  9293  9294  9295  9296  9297  9298  9299  9300
##  [9301]  9301  9302  9303  9304  9305  9306  9307  9308  9309  9310  9311  9312
##  [9313]  9313  9314  9315  9316  9317  9318  9319  9320  9321  9322  9323  9324
##  [9325]  9325  9326  9327  9328  9329  9330  9331  9332  9333  9334  9335  9336
##  [9337]  9337  9338  9339  9340  9341  9342  9343  9344  9345  9346  9347  9348
##  [9349]  9349  9350  9351  9352  9353  9354  9355  9356  9357  9358  9359  9360
##  [9361]  9361  9362  9363  9364  9365  9366  9367  9368  9369  9370  9371  9372
##  [9373]  9373  9374  9375  9376  9377  9378  9379  9380  9381  9382  9383  9384
##  [9385]  9385  9386  9387  9388  9389  9390  9391  9392  9393  9394  9395  9396
##  [9397]  9397  9398  9399  9400  9401  9402  9403  9404  9405  9406  9407  9408
##  [9409]  9409  9410  9411  9412  9413  9414  9415  9416  9417  9418  9419  9420
##  [9421]  9421  9422  9423  9424  9425  9426  9427  9428  9429  9430  9431  9432
##  [9433]  9433  9434  9435  9436  9437  9438  9439  9440  9441  9442  9443  9444
##  [9445]  9445  9446  9447  9448  9449  9450  9451  9452  9453  9454  9455  9456
##  [9457]  9457  9458  9459  9460  9461  9462  9463  9464  9465  9466  9467  9468
##  [9469]  9469  9470  9471  9472  9473  9474  9475  9476  9477  9478  9479  9480
##  [9481]  9481  9482  9483  9484  9485  9486  9487  9488  9489  9490  9491  9492
##  [9493]  9493  9494  9495  9496  9497  9498  9499  9500  9501  9502  9503  9504
##  [9505]  9505  9506  9507  9508  9509  9510  9511  9512  9513  9514  9515  9516
##  [9517]  9517  9518  9519  9520  9521  9522  9523  9524  9525  9526  9527  9528
##  [9529]  9529  9530  9531  9532  9533  9534  9535  9536  9537  9538  9539  9540
##  [9541]  9541  9542  9543  9544  9545  9546  9547  9548  9549  9550  9551  9552
##  [9553]  9553  9554  9555  9556  9557  9558  9559  9560  9561  9562  9563  9564
##  [9565]  9565  9566  9567  9568  9569  9570  9571  9572  9573  9574  9575  9576
##  [9577]  9577  9578  9579  9580  9581  9582  9583  9584  9585  9586  9587  9588
##  [9589]  9589  9590  9591  9592  9593  9594  9595  9596  9597  9598  9599  9600
##  [9601]  9601  9602  9603  9604  9605  9606  9607  9608  9609  9610  9611  9612
##  [9613]  9613  9614  9615  9616  9617  9618  9619  9620  9621  9622  9623  9624
##  [9625]  9625  9626  9627  9628  9629  9630  9631  9632  9633  9634  9635  9636
##  [9637]  9637  9638  9639  9640  9641  9642  9643  9644  9645  9646  9647  9648
##  [9649]  9649  9650  9651  9652  9653  9654  9655  9656  9657  9658  9659  9660
##  [9661]  9661  9662  9663  9664  9665  9666  9667  9668  9669  9670  9671  9672
##  [9673]  9673  9674  9675  9676  9677  9678  9679  9680  9681  9682  9683  9684
##  [9685]  9685  9686  9687  9688  9689  9690  9691  9692  9693  9694  9695  9696
##  [9697]  9697  9698  9699  9700  9701  9702  9703  9704  9705  9706  9707  9708
##  [9709]  9709  9710  9711  9712  9713  9714  9715  9716  9717  9718  9719  9720
##  [9721]  9721  9722  9723  9724  9725  9726  9727  9728  9729  9730  9731  9732
##  [9733]  9733  9734  9735  9736  9737  9738  9739  9740  9741  9742  9743  9744
##  [9745]  9745  9746  9747  9748  9749  9750  9751  9752  9753  9754  9755  9756
##  [9757]  9757  9758  9759  9760  9761  9762  9763  9764  9765  9766  9767  9768
##  [9769]  9769  9770  9771  9772  9773  9774  9775  9776  9777  9778  9779  9780
##  [9781]  9781  9782  9783  9784  9785  9786  9787  9788  9789  9790  9791  9792
##  [9793]  9793  9794  9795  9796  9797  9798  9799  9800  9801  9802  9803  9804
##  [9805]  9805  9806  9807  9808  9809  9810  9811  9812  9813  9814  9815  9816
##  [9817]  9817  9818  9819  9820  9821  9822  9823  9824  9825  9826  9827  9828
##  [9829]  9829  9830  9831  9832  9833  9834  9835  9836  9837  9838  9839  9840
##  [9841]  9841  9842  9843  9844  9845  9846  9847  9848  9849  9850  9851  9852
##  [9853]  9853  9854  9855  9856  9857  9858  9859  9860  9861  9862  9863  9864
##  [9865]  9865  9866  9867  9868  9869  9870  9871  9872  9873  9874  9875  9876
##  [9877]  9877  9878  9879  9880  9881  9882  9883  9884  9885  9886  9887  9888
##  [9889]  9889  9890  9891  9892  9893  9894  9895  9896  9897  9898  9899  9900
##  [9901]  9901  9902  9903  9904  9905  9906  9907  9908  9909  9910  9911  9912
##  [9913]  9913  9914  9915  9916  9917  9918  9919  9920  9921  9922  9923  9924
##  [9925]  9925  9926  9927  9928  9929  9930  9931  9932  9933  9934  9935  9936
##  [9937]  9937  9938  9939  9940  9941  9942  9943  9944  9945  9946  9947  9948
##  [9949]  9949  9950  9951  9952  9953  9954  9955  9956  9957  9958  9959  9960
##  [9961]  9961  9962  9963  9964  9965  9966  9967  9968  9969  9970  9971  9972
##  [9973]  9973  9974  9975  9976  9977  9978  9979  9980  9981  9982  9983  9984
##  [9985]  9985  9986  9987  9988  9989  9990  9991  9992  9993  9994  9995  9996
##  [9997]  9997  9998  9999 10000 10001 10002 10003 10004 10005 10006 10007 10008
## [10009] 10009 10010 10011 10012 10013 10014 10015 10016 10017 10018 10019 10020
## [10021] 10021 10022 10023 10024 10025 10026 10027 10028 10029 10030 10031 10032
## [10033] 10033 10034 10035 10036 10037 10038 10039 10040 10041 10042 10043 10044
## [10045] 10045 10046 10047 10048 10049 10050 10051 10052 10053 10054 10055 10056
## [10057] 10057 10058 10059 10060 10061 10062 10063 10064 10065 10066 10067 10068
## [10069] 10069 10070 10071 10072 10073 10074 10075 10076 10077 10078 10079 10080
## [10081] 10081 10082 10083 10084 10085 10086 10087 10088 10089 10090 10091 10092
## [10093] 10093 10094 10095 10096 10097 10098 10099 10100 10101 10102 10103 10104
## [10105] 10105 10106 10107 10108 10109 10110 10111 10112 10113 10114 10115 10116
## [10117] 10117 10118 10119 10120 10121 10122 10123 10124 10125 10126 10127 10128
## [10129] 10129 10130 10131 10132 10133 10134 10135 10136 10137 10138 10139 10140
## [10141] 10141 10142 10143 10144 10145 10146 10147 10148 10149 10150 10151 10152
## [10153] 10153 10154 10155 10156 10157 10158 10159 10160 10161 10162 10163 10164
## [10165] 10165 10166 10167 10168 10169 10170 10171 10172 10173 10174 10175 10176
## [10177] 10177 10178 10179 10180 10181 10182 10183 10184 10185 10186 10187 10188
## [10189] 10189 10190 10191 10192 10193 10194 10195 10196 10197 10198 10199 10200
## [10201] 10201 10202 10203 10204 10205 10206 10207 10208 10209 10210 10211 10212
## [10213] 10213 10214 10215 10216 10217 10218 10219 10220 10221 10222 10223 10224
## [10225] 10225 10226 10227 10228 10229 10230 10231 10232 10233 10234 10235 10236
## [10237] 10237 10238 10239 10240 10241 10242 10243 10244 10245 10246 10247 10248
## [10249] 10249 10250 10251 10252 10253 10254 10255 10256 10257 10258 10259 10260
## [10261] 10261 10262 10263 10264 10265 10266 10267 10268 10269 10270 10271 10272
## [10273] 10273 10274 10275 10276 10277 10278 10279 10280 10281 10282 10283 10284
## [10285] 10285 10286 10287 10288 10289 10290 10291 10292 10293 10294 10295 10296
## [10297] 10297 10298 10299 10300 10301 10302 10303 10304 10305 10306 10307 10308
## [10309] 10309 10310 10311 10312 10313 10314 10315 10316 10317 10318 10319 10320
## [10321] 10321 10322 10323 10324 10325 10326 10327 10328 10329 10330 10331 10332
## [10333] 10333 10334 10335 10336 10337 10338 10339 10340 10341 10342 10343 10344
## [10345] 10345 10346 10347 10348 10349 10350 10351 10352 10353 10354 10355 10356
## [10357] 10357 10358 10359 10360 10361 10362 10363 10364 10365 10366 10367 10368
## [10369] 10369 10370 10371 10372 10373 10374 10375 10376 10377 10378 10379 10380
## [10381] 10381 10382 10383 10384 10385 10386 10387 10388 10389 10390 10391 10392
## [10393] 10393 10394 10395 10396 10397 10398 10399 10400 10401 10402 10403 10404
## [10405] 10405 10406 10407 10408 10409 10410 10411 10412 10413 10414 10415 10416
## [10417] 10417 10418 10419 10420 10421 10422 10423 10424 10425 10426 10427 10428
## [10429] 10429 10430 10431 10432 10433 10434 10435 10436 10437 10438 10439 10440
## [10441] 10441 10442 10443 10444 10445 10446 10447 10448 10449 10450 10451 10452
## [10453] 10453 10454 10455 10456 10457 10458 10459 10460 10461 10462 10463 10464
## [10465] 10465 10466 10467 10468 10469 10470 10471 10472 10473 10474 10475 10476
## [10477] 10477 10478 10479 10480 10481 10482 10483 10484 10485 10486 10487 10488
## [10489] 10489 10490 10491 10492 10493 10494 10495 10496 10497 10498 10499 10500
## [10501] 10501 10502 10503 10504 10505 10506 10507 10508 10509 10510 10511 10512
## [10513] 10513 10514 10515 10516 10517 10518 10519 10520 10521 10522 10523 10524
## [10525] 10525 10526 10527 10528 10529 10530 10531 10532 10533 10534 10535 10536
## [10537] 10537 10538 10539 10540 10541 10542 10543 10544 10545 10546 10547 10548
## [10549] 10549 10550 10551 10552 10553 10554 10555 10556 10557 10558 10559 10560
## [10561] 10561 10562 10563 10564 10565 10566 10567 10568 10569 10570 10571 10572
## [10573] 10573 10574 10575 10576 10577 10578 10579 10580 10581 10582 10583 10584
## [10585] 10585 10586 10587 10588 10589 10590 10591 10592 10593 10594 10595 10596
## [10597] 10597 10598 10599 10600 10601 10602 10603 10604 10605 10606 10607 10608
## [10609] 10609 10610 10611 10612 10613 10614 10615 10616 10617 10618 10619 10620
## [10621] 10621 10622 10623 10624 10625 10626 10627 10628 10629 10630 10631 10632
## [10633] 10633 10634 10635 10636 10637 10638 10639 10640 10641 10642 10643 10644
## [10645] 10645 10646 10647 10648 10649 10650 10651 10652 10653 10654 10655 10656
## [10657] 10657 10658 10659 10660 10661 10662 10663 10664 10665 10666 10667 10668
## [10669] 10669 10670 10671 10672 10673 10674 10675 10676 10677 10678 10679 10680
## [10681] 10681 10682 10683 10684 10685 10686 10687 10688 10689 10690 10691 10692
## [10693] 10693 10694 10695 10696 10697 10698 10699 10700 10701 10702 10703 10704
## [10705] 10705 10706 10707 10708 10709 10710 10711 10712 10713 10714 10715 10716
## [10717] 10717 10718 10719 10720 10721 10722 10723 10724 10725 10726 10727 10728
## [10729] 10729 10730 10731 10732 10733 10734 10735 10736 10737 10738 10739 10740
## [10741] 10741 10742 10743 10744 10745 10746 10747 10748 10749 10750 10751 10752
## [10753] 10753 10754 10755 10756 10757 10758 10759 10760 10761 10762 10763 10764
## [10765] 10765 10766 10767 10768 10769 10770 10771 10772 10773 10774 10775 10776
## [10777] 10777 10778 10779 10780 10781 10782 10783 10784 10785 10786 10787 10788
## [10789] 10789 10790 10791 10792 10793 10794 10795 10796 10797 10798 10799 10800
## [10801] 10801 10802 10803 10804 10805 10806 10807 10808 10809 10810 10811 10812
## [10813] 10813 10814 10815 10816 10817 10818 10819 10820 10821 10822 10823 10824
## [10825] 10825 10826 10827 10828 10829 10830 10831 10832 10833 10834 10835 10836
## [10837] 10837 10838 10839 10840 10841 10842 10843 10844 10845 10846 10847 10848
## [10849] 10849 10850 10851 10852 10853 10854 10855 10856 10857 10858 10859 10860
## [10861] 10861 10862 10863 10864 10865 10866 10867 10868 10869 10870 10871 10872
## [10873] 10873 10874 10875 10876 10877 10878 10879 10880 10881 10882 10883 10884
## [10885] 10885 10886 10887 10888 10889 10890 10891 10892 10893 10894 10895 10896
## [10897] 10897 10898 10899 10900 10901 10902 10903 10904 10905 10906 10907 10908
## [10909] 10909 10910 10911 10912 10913 10914 10915 10916 10917 10918 10919 10920
## [10921] 10921 10922 10923 10924 10925 10926 10927 10928 10929 10930 10931 10932
## [10933] 10933 10934 10935 10936 10937 10938 10939 10940 10941 10942 10943 10944
## [10945] 10945 10946 10947 10948 10949 10950 10951 10952 10953 10954 10955 10956
## [10957] 10957 10958 10959 10960 10961 10962 10963 10964 10965 10966 10967 10968
## [10969] 10969 10970 10971 10972 10973 10974 10975 10976 10977 10978 10979 10980
## [10981] 10981 10982 10983 10984 10985 10986 10987 10988 10989 10990 10991 10992
## [10993] 10993 10994 10995 10996 10997 10998 10999 11000 11001 11002 11003 11004
## [11005] 11005 11006 11007 11008 11009 11010 11011 11012 11013 11014 11015 11016
## [11017] 11017 11018 11019 11020 11021 11022 11023 11024 11025 11026 11027 11028
## [11029] 11029 11030 11031 11032 11033 11034 11035 11036 11037 11038 11039 11040
## [11041] 11041 11042 11043 11044 11045 11046 11047 11048 11049 11050 11051 11052
## [11053] 11053 11054 11055 11056 11057 11058 11059 11060 11061 11062 11063 11064
## [11065] 11065 11066 11067 11068 11069 11070 11071 11072 11073 11074 11075 11076
## [11077] 11077 11078 11079 11080 11081 11082 11083 11084 11085 11086 11087 11088
## [11089] 11089 11090 11091 11092 11093 11094 11095 11096 11097 11098 11099 11100
## [11101] 11101 11102 11103 11104 11105 11106 11107 11108 11109 11110 11111 11112
## [11113] 11113 11114 11115 11116 11117 11118 11119 11120 11121 11122 11123 11124
## [11125] 11125 11126 11127 11128 11129 11130 11131 11132 11133 11134 11135 11136
## [11137] 11137 11138 11139 11140 11141 11142 11143 11144 11145 11146 11147 11148
## [11149] 11149 11150 11151 11152 11153 11154 11155 11156 11157 11158 11159 11160
## [11161] 11161 11162 11163 11164 11165 11166 11167 11168 11169 11170 11171 11172
## [11173] 11173 11174 11175 11176 11177 11178 11179 11180 11181 11182 11183 11184
## [11185] 11185 11186 11187 11188 11189 11190 11191 11192 11193 11194 11195 11196
## [11197] 11197 11198 11199 11200 11201 11202 11203 11204 11205 11206 11207 11208
## [11209] 11209 11210 11211 11212 11213 11214 11215 11216 11217 11218 11219 11220
## [11221] 11221 11222 11223 11224 11225 11226 11227 11228 11229 11230 11231 11232
## [11233] 11233 11234 11235 11236 11237 11238 11239 11240 11241 11242 11243 11244
## [11245] 11245 11246 11247 11248 11249 11250 11251 11252 11253 11254 11255 11256
## [11257] 11257 11258 11259 11260 11261 11262 11263 11264 11265 11266 11267 11268
## [11269] 11269 11270 11271 11272 11273 11274 11275 11276 11277 11278 11279 11280
## [11281] 11281 11282 11283 11284 11285 11286 11287 11288 11289 11290 11291 11292
## [11293] 11293 11294 11295 11296 11297 11298 11299 11300 11301 11302 11303 11304
## [11305] 11305 11306 11307 11308 11309 11310 11311 11312 11313 11314 11315 11316
## [11317] 11317 11318 11319 11320 11321 11322 11323 11324 11325 11326 11327 11328
## [11329] 11329 11330 11331 11332 11333 11334 11335 11336 11337 11338 11339 11340
## [11341] 11341 11342 11343 11344 11345 11346 11347 11348 11349 11350 11351 11352
## [11353] 11353 11354 11355 11356 11357 11358 11359 11360 11361 11362 11363 11364
## [11365] 11365 11366 11367 11368 11369 11370 11371 11372 11373 11374 11375 11376
## [11377] 11377 11378 11379 11380 11381 11382 11383 11384 11385 11386 11387 11388
## [11389] 11389 11390 11391 11392 11393 11394 11395 11396 11397 11398 11399 11400
## [11401] 11401 11402 11403 11404 11405 11406 11407 11408 11409 11410 11411 11412
## [11413] 11413 11414 11415 11416 11417 11418 11419 11420 11421 11422 11423 11424
## [11425] 11425 11426 11427 11428 11429 11430 11431 11432 11433 11434 11435 11436
## [11437] 11437 11438 11439 11440 11441 11442 11443 11444 11445 11446 11447 11448
## [11449] 11449 11450 11451 11452 11453 11454 11455 11456 11457 11458 11459 11460
## [11461] 11461 11462 11463 11464 11465 11466 11467 11468 11469 11470 11471 11472
## [11473] 11473 11474 11475 11476 11477 11478 11479 11480 11481 11482 11483 11484
## [11485] 11485 11486 11487 11488 11489 11490 11491 11492 11493 11494 11495 11496
## [11497] 11497 11498 11499 11500 11501 11502 11503 11504 11505 11506 11507 11508
## [11509] 11509 11510 11511 11512 11513 11514 11515 11516 11517 11518 11519 11520
## [11521] 11521 11522 11523 11524 11525 11526 11527 11528 11529 11530 11531 11532
## [11533] 11533 11534 11535 11536 11537 11538 11539 11540 11541 11542 11543 11544
## [11545] 11545 11546 11547 11548 11549 11550 11551 11552 11553 11554 11555 11556
## [11557] 11557 11558 11559 11560 11561 11562 11563 11564 11565 11566 11567 11568
## [11569] 11569 11570 11571 11572 11573 11574 11575 11576 11577 11578 11579 11580
## [11581] 11581 11582 11583 11584 11585 11586 11587 11588 11589 11590 11591 11592
## [11593] 11593 11594 11595 11596 11597 11598 11599 11600 11601 11602 11603 11604
## [11605] 11605 11606 11607 11608 11609 11610 11611 11612 11613 11614 11615 11616
## [11617] 11617 11618 11619 11620 11621 11622 11623 11624 11625 11626 11627 11628
## [11629] 11629 11630 11631 11632 11633 11634 11635 11636 11637 11638 11639 11640
## [11641] 11641 11642 11643 11644 11645 11646 11647 11648 11649 11650 11651 11652
## [11653] 11653 11654 11655 11656 11657 11658 11659 11660 11661 11662 11663 11664
## [11665] 11665 11666 11667 11668 11669 11670 11671 11672 11673 11674 11675 11676
## [11677] 11677 11678 11679 11680 11681 11682 11683 11684 11685 11686 11687 11688
## [11689] 11689 11690 11691 11692 11693 11694 11695 11696 11697 11698 11699 11700
## [11701] 11701 11702 11703 11704 11705 11706 11707 11708 11709 11710 11711 11712
## [11713] 11713 11714 11715 11716 11717 11718 11719 11720 11721 11722 11723 11724
## [11725] 11725 11726 11727 11728 11729 11730 11731 11732 11733 11734 11735 11736
## [11737] 11737 11738 11739 11740 11741 11742 11743 11744 11745 11746 11747 11748
## [11749] 11749 11750 11751 11752 11753 11754 11755 11756 11757 11758 11759 11760
## [11761] 11761 11762 11763 11764 11765 11766 11767 11768 11769 11770 11771 11772
## [11773] 11773 11774 11775 11776 11777 11778 11779 11780 11781 11782 11783 11784
## [11785] 11785 11786 11787 11788 11789 11790 11791 11792 11793 11794 11795 11796
## [11797] 11797 11798 11799 11800 11801 11802 11803 11804 11805 11806 11807 11808
## [11809] 11809 11810 11811 11812 11813 11814 11815 11816 11817 11818 11819 11820
## [11821] 11821 11822 11823 11824 11825 11826 11827 11828 11829 11830 11831 11832
## [11833] 11833 11834 11835 11836 11837 11838 11839 11840 11841 11842 11843 11844
## [11845] 11845 11846 11847 11848 11849 11850 11851 11852 11853 11854 11855 11856
## [11857] 11857 11858 11859 11860 11861 11862 11863 11864 11865 11866 11867 11868
## [11869] 11869 11870 11871 11872 11873 11874 11875 11876 11877 11878 11879 11880
## [11881] 11881 11882 11883 11884 11885 11886 11887 11888 11889 11890 11891 11892
## [11893] 11893 11894 11895 11896 11897 11898 11899 11900 11901 11902 11903 11904
## [11905] 11905 11906 11907 11908 11909 11910 11911 11912 11913 11914 11915 11916
## [11917] 11917 11918 11919 11920 11921 11922 11923 11924 11925 11926 11927 11928
## [11929] 11929 11930 11931 11932 11933 11934 11935 11936 11937 11938 11939 11940
## [11941] 11941 11942 11943 11944 11945 11946 11947 11948 11949 11950 11951 11952
## [11953] 11953 11954 11955 11956 11957 11958 11959 11960 11961 11962 11963 11964
## [11965] 11965 11966 11967 11968 11969 11970 11971 11972 11973 11974 11975 11976
## [11977] 11977 11978 11979 11980 11981 11982 11983 11984 11985 11986 11987 11988
## [11989] 11989 11990 11991 11992 11993 11994 11995 11996 11997 11998 11999 12000
## [12001] 12001 12002 12003 12004 12005 12006 12007 12008 12009 12010 12011 12012
## [12013] 12013 12014 12015 12016 12017 12018 12019 12020 12021 12022 12023 12024
## [12025] 12025 12026 12027 12028 12029 12030 12031 12032 12033 12034 12035 12036
## [12037] 12037 12038 12039 12040 12041 12042 12043 12044 12045 12046 12047 12048
## [12049] 12049 12050 12051 12052 12053 12054 12055 12056 12057 12058 12059 12060
## [12061] 12061 12062 12063 12064 12065 12066 12067 12068 12069 12070 12071 12072
## [12073] 12073 12074 12075 12076 12077 12078 12079 12080 12081 12082 12083 12084
## [12085] 12085 12086 12087 12088 12089 12090 12091 12092 12093 12094 12095 12096
## [12097] 12097 12098 12099 12100 12101 12102 12103 12104 12105 12106 12107 12108
## [12109] 12109 12110 12111 12112 12113 12114 12115 12116 12117 12118 12119 12120
## [12121] 12121 12122 12123 12124 12125 12126 12127 12128 12129 12130 12131 12132
## [12133] 12133 12134 12135 12136 12137 12138 12139 12140 12141 12142 12143 12144
## [12145] 12145 12146 12147 12148 12149 12150 12151 12152 12153 12154 12155 12156
## [12157] 12157 12158 12159 12160 12161 12162 12163 12164 12165 12166 12167 12168
## [12169] 12169 12170 12171 12172 12173 12174 12175 12176 12177 12178 12179 12180
## [12181] 12181 12182 12183 12184 12185 12186 12187 12188 12189 12190 12191 12192
## [12193] 12193 12194 12195 12196 12197 12198 12199 12200 12201 12202 12203 12204
## [12205] 12205 12206 12207 12208 12209 12210 12211 12212 12213 12214 12215 12216
## [12217] 12217 12218 12219 12220 12221 12222 12223 12224 12225 12226 12227 12228
## [12229] 12229 12230 12231 12232 12233 12234 12235 12236 12237 12238 12239 12240
## [12241] 12241 12242 12243 12244 12245 12246 12247 12248 12249 12250 12251 12252
## [12253] 12253 12254 12255 12256 12257 12258 12259 12260 12261 12262 12263 12264
## [12265] 12265 12266 12267 12268 12269 12270 12271 12272 12273 12274 12275 12276
## [12277] 12277 12278 12279 12280 12281 12282 12283 12284 12285 12286 12287 12288
## [12289] 12289 12290 12291 12292 12293 12294 12295 12296 12297 12298 12299 12300
## [12301] 12301 12302 12303 12304 12305 12306 12307 12308 12309 12310 12311 12312
## [12313] 12313 12314 12315 12316 12317 12318 12319 12320 12321 12322 12323 12324
## [12325] 12325 12326 12327 12328 12329 12330 12331 12332 12333 12334 12335 12336
## [12337] 12337 12338 12339 12340 12341 12342 12343 12344 12345 12346 12347 12348
## [12349] 12349 12350 12351 12352 12353 12354 12355 12356 12357 12358 12359 12360
## [12361] 12361 12362 12363 12364 12365 12366 12367 12368 12369 12370 12371 12372
## [12373] 12373 12374 12375 12376 12377 12378 12379 12380 12381 12382 12383 12384
## [12385] 12385 12386 12387 12388 12389 12390 12391 12392 12393 12394 12395 12396
## [12397] 12397 12398 12399 12400 12401 12402 12403 12404 12405 12406 12407 12408
## [12409] 12409 12410 12411 12412 12413 12414 12415 12416 12417 12418 12419 12420
## [12421] 12421 12422 12423 12424 12425 12426 12427 12428 12429 12430 12431 12432
## [12433] 12433 12434 12435 12436 12437 12438 12439 12440 12441 12442 12443 12444
## [12445] 12445 12446 12447 12448 12449 12450 12451 12452 12453 12454 12455 12456
## [12457] 12457 12458 12459 12460 12461 12462 12463 12464 12465 12466 12467 12468
## [12469] 12469 12470 12471 12472 12473 12474 12475 12476 12477 12478 12479 12480
## [12481] 12481 12482 12483 12484 12485 12486 12487 12488 12489 12490 12491 12492
## [12493] 12493 12494 12495 12496 12497 12498 12499 12500 12501 12502 12503 12504
## [12505] 12505 12506 12507 12508 12509 12510 12511 12512 12513 12514 12515 12516
## [12517] 12517 12518 12519 12520 12521 12522 12523 12524 12525 12526 12527 12528
## [12529] 12529 12530 12531 12532 12533 12534 12535 12536 12537 12538 12539 12540
## [12541] 12541 12542 12543 12544 12545 12546 12547 12548 12549 12550 12551 12552
## [12553] 12553 12554 12555 12556 12557 12558 12559 12560 12561 12562 12563 12564
## [12565] 12565 12566 12567 12568 12569 12570 12571 12572 12573 12574 12575 12576
## [12577] 12577 12578 12579 12580 12581 12582 12583 12584 12585 12586 12587 12588
## [12589] 12589 12590 12591 12592 12593 12594 12595 12596 12597 12598 12599 12600
## [12601] 12601 12602 12603 12604 12605 12606 12607 12608 12609 12610 12611 12612
## [12613] 12613 12614 12615 12616 12617 12618 12619 12620 12621 12622 12623 12624
## [12625] 12625 12626 12627 12628 12629 12630 12631 12632 12633 12634 12635 12636
## [12637] 12637 12638 12639 12640 12641 12642 12643 12644 12645 12646 12647 12648
## [12649] 12649 12650 12651 12652 12653 12654 12655 12656 12657 12658 12659 12660
## [12661] 12661 12662 12663 12664 12665 12666 12667 12668 12669 12670 12671 12672
## [12673] 12673 12674 12675 12676 12677 12678 12679 12680 12681 12682 12683 12684
## [12685] 12685 12686 12687 12688 12689 12690 12691 12692 12693 12694 12695 12696
## [12697] 12697 12698 12699 12700 12701 12702 12703 12704 12705 12706 12707 12708
## [12709] 12709 12710 12711 12712 12713 12714 12715 12716 12717 12718 12719 12720
## [12721] 12721 12722 12723 12724 12725 12726 12727 12728 12729 12730 12731 12732
## [12733] 12733 12734 12735 12736 12737 12738 12739 12740 12741 12742 12743 12744
## [12745] 12745 12746 12747 12748 12749 12750 12751 12752 12753 12754 12755 12756
## [12757] 12757 12758 12759 12760 12761 12762 12763 12764 12765 12766 12767 12768
## [12769] 12769 12770 12771 12772 12773 12774 12775 12776 12777 12778 12779 12780
## [12781] 12781 12782 12783 12784 12785 12786 12787 12788 12789 12790 12791 12792
## [12793] 12793 12794 12795 12796 12797 12798 12799 12800 12801 12802 12803 12804
## [12805] 12805 12806 12807 12808 12809 12810 12811 12812 12813 12814 12815 12816
## [12817] 12817 12818 12819 12820 12821 12822 12823 12824 12825 12826 12827 12828
## [12829] 12829 12830 12831 12832 12833 12834 12835 12836 12837 12838 12839 12840
## [12841] 12841 12842 12843 12844 12845 12846 12847 12848 12849 12850 12851 12852
## [12853] 12853 12854 12855 12856 12857 12858 12859 12860 12861 12862 12863 12864
## [12865] 12865 12866 12867 12868 12869 12870 12871 12872 12873 12874 12875 12876
## [12877] 12877 12878 12879 12880 12881 12882 12883 12884 12885 12886 12887 12888
## [12889] 12889 12890 12891 12892 12893 12894 12895 12896 12897 12898 12899 12900
## [12901] 12901 12902 12903 12904 12905 12906 12907 12908 12909 12910 12911 12912
## [12913] 12913 12914 12915 12916 12917 12918 12919 12920 12921 12922 12923 12924
## [12925] 12925 12926 12927 12928 12929 12930 12931 12932 12933 12934 12935 12936
## [12937] 12937 12938 12939 12940 12941 12942 12943 12944 12945 12946 12947 12948
## [12949] 12949 12950 12951 12952 12953 12954 12955 12956 12957 12958 12959 12960
## [12961] 12961 12962 12963 12964 12965 12966 12967 12968 12969 12970 12971 12972
## [12973] 12973 12974 12975 12976 12977 12978 12979 12980 12981 12982 12983 12984
## [12985] 12985 12986 12987 12988 12989 12990 12991 12992 12993 12994 12995 12996
## [12997] 12997 12998 12999 13000 13001 13002 13003 13004 13005 13006 13007 13008
## [13009] 13009 13010 13011 13012 13013 13014 13015 13016 13017 13018 13019 13020
## [13021] 13021 13022 13023 13024 13025 13026 13027 13028 13029 13030 13031 13032
## [13033] 13033 13034 13035 13036 13037 13038 13039 13040 13041 13042 13043 13044
## [13045] 13045 13046 13047 13048 13049 13050 13051 13052 13053 13054 13055 13056
## [13057] 13057 13058 13059 13060 13061 13062 13063 13064 13065 13066 13067 13068
## [13069] 13069 13070 13071 13072 13073 13074 13075 13076 13077 13078 13079 13080
## [13081] 13081 13082 13083 13084 13085 13086 13087 13088 13089 13090 13091 13092
## [13093] 13093 13094 13095 13096 13097 13098 13099 13100 13101 13102 13103 13104
## [13105] 13105 13106 13107 13108 13109 13110 13111 13112 13113 13114 13115 13116
## [13117] 13117 13118 13119 13120 13121 13122 13123 13124 13125 13126 13127 13128
## [13129] 13129 13130 13131 13132 13133 13134 13135 13136 13137 13138 13139 13140
## [13141] 13141 13142 13143 13144 13145 13146 13147 13148 13149 13150 13151 13152
## [13153] 13153 13154 13155 13156 13157 13158 13159 13160 13161 13162 13163 13164
## [13165] 13165 13166 13167 13168 13169 13170 13171 13172 13173 13174 13175 13176
## [13177] 13177 13178 13179 13180 13181 13182 13183 13184 13185 13186 13187 13188
## [13189] 13189 13190 13191 13192 13193 13194 13195 13196 13197 13198 13199 13200
## [13201] 13201 13202 13203 13204 13205 13206 13207 13208 13209 13210 13211 13212
## [13213] 13213 13214 13215 13216 13217 13218 13219 13220 13221 13222 13223 13224
## [13225] 13225 13226 13227 13228 13229 13230 13231 13232 13233 13234 13235 13236
## [13237] 13237 13238 13239 13240 13241 13242 13243 13244 13245 13246 13247 13248
## [13249] 13249 13250 13251 13252 13253 13254 13255 13256 13257 13258 13259 13260
## [13261] 13261 13262 13263 13264 13265 13266 13267 13268 13269 13270 13271 13272
## [13273] 13273 13274 13275 13276 13277 13278 13279 13280 13281 13282 13283 13284
## [13285] 13285 13286 13287 13288 13289 13290 13291 13292 13293 13294 13295 13296
## [13297] 13297 13298 13299 13300 13301 13302 13303 13304 13305 13306 13307 13308
## [13309] 13309 13310 13311 13312 13313 13314 13315 13316 13317 13318 13319 13320
## [13321] 13321 13322 13323 13324 13325 13326 13327 13328 13329 13330 13331 13332
## [13333] 13333 13334 13335 13336 13337 13338 13339 13340 13341 13342 13343 13344
## [13345] 13345 13346 13347 13348 13349 13350 13351 13352 13353 13354 13355 13356
## [13357] 13357 13358 13359 13360 13361 13362 13363 13364 13365 13366 13367 13368
## [13369] 13369 13370 13371 13372 13373 13374 13375 13376 13377 13378 13379 13380
## [13381] 13381 13382 13383 13384 13385 13386 13387 13388 13389 13390 13391 13392
## [13393] 13393 13394 13395 13396 13397 13398 13399 13400 13401 13402 13403 13404
## [13405] 13405 13406 13407 13408 13409 13410 13411 13412 13413 13414 13415 13416
## [13417] 13417 13418 13419 13420 13421 13422 13423 13424 13425 13426 13427 13428
## [13429] 13429 13430 13431 13432 13433 13434 13435 13436 13437 13438 13439 13440
## [13441] 13441 13442 13443 13444 13445 13446 13447 13448 13449 13450 13451 13452
## [13453] 13453 13454 13455 13456 13457 13458 13459 13460 13461 13462 13463 13464
## [13465] 13465 13466 13467 13468 13469 13470 13471 13472 13473 13474 13475 13476
## [13477] 13477 13478 13479 13480 13481 13482 13483 13484 13485 13486 13487 13488
## [13489] 13489 13490 13491 13492 13493 13494 13495 13496 13497 13498 13499 13500
## [13501] 13501 13502 13503 13504 13505 13506 13507 13508 13509 13510 13511 13512
## [13513] 13513 13514 13515 13516 13517 13518 13519 13520 13521 13522 13523 13524
## [13525] 13525 13526 13527 13528 13529 13530 13531 13532 13533 13534 13535 13536
## [13537] 13537 13538 13539 13540 13541 13542 13543 13544 13545 13546 13547 13548
## [13549] 13549 13550 13551 13552 13553 13554 13555 13556 13557 13558 13559 13560
## [13561] 13561 13562 13563 13564 13565 13566 13567 13568 13569 13570 13571 13572
## [13573] 13573 13574 13575 13576 13577 13578 13579 13580 13581 13582 13583 13584
## [13585] 13585 13586 13587 13588 13589 13590 13591 13592 13593 13594 13595 13596
## [13597] 13597 13598 13599 13600 13601 13602 13603 13604 13605 13606 13607 13608
## [13609] 13609 13610 13611 13612 13613 13614 13615 13616 13617 13618 13619 13620
## [13621] 13621 13622 13623 13624 13625 13626 13627 13628 13629 13630 13631 13632
## [13633] 13633 13634 13635 13636 13637 13638 13639 13640 13641 13642 13643 13644
## [13645] 13645 13646 13647 13648 13649 13650 13651 13652 13653 13654 13655 13656
## [13657] 13657 13658 13659 13660 13661 13662 13663 13664 13665 13666 13667 13668
## [13669] 13669 13670 13671 13672 13673 13674 13675 13676 13677 13678 13679 13680
## [13681] 13681 13682 13683 13684 13685 13686 13687 13688 13689 13690 13691 13692
## [13693] 13693 13694 13695 13696 13697 13698 13699 13700 13701 13702 13703 13704
## [13705] 13705 13706 13707 13708 13709 13710 13711 13712 13713 13714 13715 13716
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## [13729] 13729 13730 13731 13732 13733 13734 13735 13736 13737 13738 13739 13740
## [13741] 13741 13742 13743 13744 13745 13746 13747 13748 13749 13750 13751 13752
## [13753] 13753 13754 13755 13756 13757 13758 13759 13760 13761 13762 13763 13764
## [13765] 13765 13766 13767 13768 13769 13770 13771 13772 13773 13774 13775 13776
## [13777] 13777 13778 13779 13780 13781 13782 13783 13784 13785 13786 13787 13788
## [13789] 13789 13790 13791 13792 13793 13794 13795 13796 13797 13798 13799 13800
## [13801] 13801 13802 13803 13804 13805 13806 13807 13808 13809 13810 13811 13812
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## [13825] 13825 13826 13827 13828 13829 13830 13831 13832 13833 13834 13835 13836
## [13837] 13837 13838 13839 13840 13841 13842 13843 13844 13845 13846 13847 13848
## [13849] 13849 13850 13851 13852 13853 13854 13855 13856 13857 13858 13859 13860
## [13861] 13861 13862 13863 13864 13865 13866 13867 13868 13869 13870 13871 13872
## [13873] 13873 13874 13875 13876 13877 13878 13879 13880 13881 13882 13883 13884
## [13885] 13885 13886 13887 13888 13889 13890 13891 13892 13893 13894 13895 13896
## [13897] 13897 13898 13899 13900 13901 13902 13903 13904 13905 13906 13907 13908
## [13909] 13909 13910 13911 13912 13913 13914 13915 13916 13917 13918 13919 13920
## [13921] 13921 13922 13923 13924 13925 13926 13927 13928 13929 13930 13931 13932
## [13933] 13933 13934 13935 13936 13937 13938 13939 13940 13941 13942 13943 13944
## [13945] 13945 13946 13947 13948 13949 13950 13951 13952 13953 13954 13955 13956
## [13957] 13957 13958 13959 13960 13961 13962 13963 13964 13965 13966 13967 13968
## [13969] 13969 13970 13971 13972 13973 13974 13975 13976 13977 13978 13979 13980
## [13981] 13981 13982 13983 13984 13985 13986 13987 13988 13989 13990 13991 13992
## [13993] 13993 13994 13995 13996 13997 13998 13999 14000 14001 14002 14003 14004
## [14005] 14005 14006 14007 14008 14009 14010 14011 14012 14013 14014 14015 14016
## [14017] 14017 14018 14019 14020 14021 14022 14023 14024 14025 14026 14027 14028
## [14029] 14029 14030 14031 14032 14033 14034 14035 14036 14037 14038 14039 14040
## [14041] 14041 14042 14043 14044 14045 14046 14047 14048 14049 14050 14051 14052
## [14053] 14053 14054 14055 14056 14057 14058 14059 14060 14061 14062 14063 14064
## [14065] 14065 14066 14067 14068 14069 14070 14071 14072 14073 14074 14075 14076
## [14077] 14077 14078 14079 14080 14081 14082 14083 14084 14085 14086 14087 14088
## [14089] 14089 14090 14091 14092 14093 14094 14095 14096 14097 14098 14099 14100
## [14101] 14101 14102 14103 14104 14105 14106 14107 14108 14109 14110 14111 14112
## [14113] 14113 14114 14115 14116 14117 14118 14119 14120 14121 14122 14123 14124
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## [14137] 14137 14138 14139 14140 14141 14142 14143 14144 14145 14146 14147 14148
## [14149] 14149 14150 14151 14152 14153 14154 14155 14156 14157 14158 14159 14160
## [14161] 14161 14162 14163 14164 14165 14166 14167 14168 14169 14170 14171 14172
## [14173] 14173 14174 14175 14176 14177 14178 14179 14180 14181 14182 14183 14184
## [14185] 14185 14186 14187 14188 14189 14190 14191 14192 14193 14194 14195 14196
## [14197] 14197 14198 14199 14200 14201 14202 14203 14204 14205 14206 14207 14208
## [14209] 14209 14210 14211 14212 14213 14214 14215 14216 14217 14218 14219 14220
## [14221] 14221 14222 14223 14224 14225 14226 14227 14228 14229 14230 14231 14232
## [14233] 14233 14234 14235 14236 14237 14238 14239 14240 14241 14242 14243 14244
## [14245] 14245 14246 14247 14248 14249 14250 14251 14252 14253 14254 14255 14256
## [14257] 14257 14258 14259 14260 14261 14262 14263 14264 14265 14266 14267 14268
## [14269] 14269 14270 14271 14272 14273 14274 14275 14276 14277 14278 14279 14280
## [14281] 14281 14282 14283 14284 14285 14286 14287 14288 14289 14290 14291 14292
## [14293] 14293 14294 14295 14296 14297 14298 14299 14300 14301 14302 14303 14304
## [14305] 14305 14306 14307 14308 14309 14310 14311 14312 14313 14314 14315 14316
## [14317] 14317 14318 14319 14320 14321 14322 14323 14324 14325 14326 14327 14328
## [14329] 14329 14330 14331 14332 14333 14334 14335 14336 14337 14338 14339 14340
## [14341] 14341 14342 14343 14344 14345 14346 14347 14348 14349 14350 14351 14352
## [14353] 14353 14354 14355 14356 14357 14358 14359 14360 14361 14362 14363 14364
## [14365] 14365 14366 14367 14368 14369 14370 14371 14372 14373 14374 14375 14376
## [14377] 14377 14378 14379 14380 14381 14382 14383 14384 14385 14386 14387 14388
## [14389] 14389 14390 14391 14392 14393 14394 14395 14396 14397 14398 14399 14400
## [14401] 14401 14402 14403 14404 14405 14406 14407 14408 14409 14410 14411 14412
## [14413] 14413 14414 14415 14416 14417 14418 14419 14420 14421 14422 14423 14424
## [14425] 14425 14426 14427 14428 14429 14430 14431 14432 14433 14434 14435 14436
## [14437] 14437 14438 14439 14440 14441 14442 14443 14444 14445 14446 14447 14448
## [14449] 14449 14450 14451 14452 14453 14454 14455 14456 14457 14458 14459 14460
## [14461] 14461 14462 14463 14464 14465 14466 14467 14468 14469 14470 14471 14472
## [14473] 14473 14474 14475 14476 14477 14478 14479 14480 14481 14482 14483 14484
## [14485] 14485 14486 14487 14488 14489 14490 14491 14492 14493 14494 14495 14496
## [14497] 14497 14498 14499 14500 14501 14502 14503 14504 14505 14506 14507 14508
## [14509] 14509 14510 14511 14512 14513 14514 14515 14516 14517 14518 14519 14520
## [14521] 14521 14522 14523 14524 14525 14526 14527 14528 14529 14530 14531 14532
## [14533] 14533 14534 14535 14536 14537 14538 14539 14540 14541 14542 14543 14544
## [14545] 14545 14546 14547 14548 14549 14550 14551 14552 14553 14554 14555 14556
## [14557] 14557 14558 14559 14560 14561 14562 14563 14564 14565 14566 14567 14568
## [14569] 14569 14570 14571 14572 14573 14574 14575 14576 14577 14578 14579 14580
## [14581] 14581 14582 14583 14584 14585 14586 14587 14588 14589 14590 14591 14592
## [14593] 14593 14594 14595 14596 14597 14598 14599 14600 14601 14602 14603 14604
## [14605] 14605 14606 14607 14608 14609 14610 14611 14612 14613 14614 14615 14616
## [14617] 14617 14618 14619 14620 14621 14622 14623 14624 14625 14626 14627 14628
## [14629] 14629 14630 14631 14632 14633 14634 14635 14636 14637 14638 14639 14640
## [14641] 14641 14642 14643 14644 14645 14646 14647 14648 14649 14650 14651 14652
## [14653] 14653 14654 14655 14656 14657 14658 14659 14660 14661 14662 14663 14664
## [14665] 14665 14666 14667 14668 14669 14670 14671 14672 14673 14674 14675 14676
## [14677] 14677 14678 14679 14680 14681 14682 14683 14684 14685 14686 14687 14688
## [14689] 14689 14690 14691 14692 14693 14694 14695 14696 14697 14698 14699 14700
## [14701] 14701 14702 14703 14704 14705 14706 14707 14708 14709 14710 14711 14712
## [14713] 14713 14714 14715 14716 14717 14718 14719 14720 14721 14722 14723 14724
## [14725] 14725 14726 14727 14728 14729 14730 14731 14732 14733 14734 14735 14736
## [14737] 14737 14738 14739 14740 14741 14742 14743 14744 14745 14746 14747 14748
## [14749] 14749 14750 14751 14752 14753 14754 14755 14756 14757 14758 14759 14760
## [14761] 14761 14762 14763 14764 14765 14766 14767 14768 14769 14770 14771 14772
## [14773] 14773 14774 14775 14776 14777 14778 14779 14780 14781 14782 14783 14784
## [14785] 14785 14786 14787 14788 14789 14790 14791 14792 14793 14794 14795 14796
## [14797] 14797 14798 14799 14800 14801 14802 14803 14804 14805 14806 14807 14808
## [14809] 14809 14810 14811 14812 14813 14814 14815 14816 14817 14818 14819 14820
## [14821] 14821 14822 14823 14824 14825 14826 14827 14828 14829 14830 14831 14832
## [14833] 14833 14834 14835 14836 14837 14838 14839 14840 14841 14842 14843 14844
## [14845] 14845 14846 14847 14848 14849 14850 14851 14852 14853 14854 14855 14856
## [14857] 14857 14858 14859 14860 14861 14862 14863 14864 14865 14866 14867 14868
## [14869] 14869 14870 14871 14872 14873 14874 14875 14876 14877 14878 14879 14880
## [14881] 14881 14882 14883 14884 14885 14886 14887 14888 14889 14890 14891 14892
## [14893] 14893 14894 14895 14896 14897 14898 14899 14900 14901 14902 14903 14904
## [14905] 14905 14906 14907 14908 14909 14910 14911 14912 14913 14914 14915 14916
## [14917] 14917 14918 14919 14920 14921 14922 14923 14924 14925 14926 14927 14928
## [14929] 14929 14930 14931 14932 14933 14934 14935 14936 14937 14938 14939 14940
## [14941] 14941 14942 14943 14944 14945 14946 14947 14948 14949 14950 14951 14952
## [14953] 14953 14954 14955 14956 14957 14958 14959 14960 14961 14962 14963 14964
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## [15001] 15001 15002 15003 15004 15005 15006 15007 15008 15009 15010 15011 15012
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## [15037] 15037 15038 15039 15040 15041 15042 15043 15044 15045 15046 15047 15048
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## [15217] 15217 15218 15219 15220 15221 15222 15223 15224 15225 15226 15227 15228
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## [15301] 15301 15302 15303 15304 15305 15306 15307 15308 15309 15310 15311 15312
## [15313] 15313 15314 15315 15316 15317 15318 15319 15320 15321 15322 15323 15324
## [15325] 15325 15326 15327 15328 15329 15330 15331 15332 15333 15334 15335 15336
## [15337] 15337 15338 15339 15340 15341 15342 15343 15344 15345 15346 15347 15348
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## [15361] 15361 15362 15363 15364 15365 15366 15367 15368 15369 15370 15371 15372
## [15373] 15373 15374 15375 15376 15377 15378 15379 15380 15381 15382 15383 15384
## [15385] 15385 15386 15387 15388 15389 15390 15391 15392 15393 15394 15395 15396
## [15397] 15397 15398 15399 15400 15401 15402 15403 15404 15405 15406 15407 15408
## [15409] 15409 15410 15411 15412 15413 15414 15415 15416 15417 15418 15419 15420
## [15421] 15421 15422 15423 15424 15425 15426 15427 15428 15429 15430 15431 15432
## [15433] 15433 15434 15435 15436 15437 15438 15439 15440 15441 15442 15443 15444
## [15445] 15445 15446 15447 15448 15449 15450 15451 15452 15453 15454 15455 15456
## [15457] 15457 15458 15459 15460 15461 15462 15463 15464 15465 15466 15467 15468
## [15469] 15469 15470 15471 15472 15473 15474 15475 15476 15477 15478 15479 15480
## [15481] 15481 15482 15483 15484 15485 15486 15487 15488 15489 15490 15491 15492
## [15493] 15493 15494 15495 15496 15497 15498 15499 15500 15501 15502 15503 15504
## [15505] 15505 15506 15507 15508 15509 15510 15511 15512 15513 15514 15515 15516
## [15517] 15517 15518 15519 15520 15521 15522 15523 15524 15525 15526 15527 15528
## [15529] 15529 15530 15531 15532 15533 15534 15535 15536 15537 15538 15539 15540
## [15541] 15541 15542 15543 15544 15545 15546 15547 15548 15549 15550 15551 15552
## [15553] 15553 15554 15555 15556 15557 15558 15559 15560 15561 15562 15563 15564
## [15565] 15565 15566 15567 15568 15569 15570 15571 15572 15573 15574 15575 15576
## [15577] 15577 15578 15579 15580 15581 15582 15583 15584 15585 15586 15587 15588
## [15589] 15589 15590 15591 15592 15593 15594 15595 15596 15597 15598 15599 15600
## [15601] 15601 15602 15603 15604 15605 15606 15607 15608 15609 15610 15611 15612
## [15613] 15613 15614 15615 15616 15617 15618 15619 15620 15621 15622 15623 15624
## [15625] 15625 15626 15627 15628 15629 15630 15631 15632 15633 15634 15635 15636
## [15637] 15637 15638 15639 15640 15641 15642 15643 15644 15645 15646 15647 15648
## [15649] 15649 15650 15651 15652 15653 15654 15655 15656 15657 15658 15659 15660
## [15661] 15661 15662 15663 15664 15665 15666 15667 15668 15669 15670 15671 15672
## [15673] 15673 15674 15675 15676 15677 15678 15679 15680 15681 15682 15683 15684
## [15685] 15685 15686 15687 15688 15689 15690 15691 15692 15693 15694 15695 15696
## [15697] 15697 15698 15699 15700 15701 15702 15703 15704 15705 15706 15707 15708
## [15709] 15709 15710 15711 15712 15713 15714 15715 15716 15717 15718 15719 15720
## [15721] 15721 15722 15723 15724 15725 15726 15727 15728 15729 15730 15731 15732
## [15733] 15733 15734 15735 15736 15737 15738 15739 15740 15741 15742 15743 15744
## [15745] 15745 15746 15747 15748 15749 15750 15751 15752 15753 15754 15755 15756
## [15757] 15757 15758 15759 15760 15761 15762 15763 15764 15765 15766 15767 15768
## [15769] 15769 15770 15771 15772 15773 15774 15775 15776 15777 15778 15779 15780
## [15781] 15781 15782 15783 15784 15785 15786 15787 15788 15789 15790 15791 15792
## [15793] 15793 15794 15795 15796 15797 15798 15799 15800 15801 15802 15803 15804
## [15805] 15805 15806 15807 15808 15809 15810 15811 15812 15813 15814 15815 15816
## [15817] 15817 15818 15819 15820 15821 15822 15823 15824 15825 15826 15827 15828
## [15829] 15829 15830 15831 15832 15833 15834 15835 15836 15837 15838 15839 15840
## [15841] 15841 15842 15843 15844 15845 15846 15847 15848 15849 15850 15851 15852
## [15853] 15853 15854 15855 15856 15857 15858 15859 15860 15861 15862 15863 15864
## [15865] 15865 15866 15867 15868 15869 15870 15871 15872 15873 15874 15875 15876
## [15877] 15877 15878 15879 15880 15881 15882 15883 15884 15885 15886 15887 15888
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## [15901] 15901 15902 15903 15904 15905 15906 15907 15908 15909 15910 15911 15912
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## [15925] 15925 15926 15927 15928 15929 15930 15931 15932 15933 15934 15935 15936
## [15937] 15937 15938 15939 15940 15941 15942 15943 15944 15945 15946 15947 15948
## [15949] 15949 15950 15951 15952 15953 15954 15955 15956 15957 15958 15959 15960
## [15961] 15961 15962 15963 15964 15965 15966 15967 15968 15969 15970 15971 15972
## [15973] 15973 15974 15975 15976 15977 15978 15979 15980 15981 15982 15983 15984
## [15985] 15985 15986 15987 15988 15989 15990 15991 15992 15993 15994 15995 15996
## [15997] 15997 15998 15999 16000 16001 16002 16003 16004 16005 16006 16007 16008
## [16009] 16009 16010 16011 16012 16013 16014 16015 16016 16017 16018 16019 16020
## [16021] 16021 16022 16023 16024 16025 16026 16027 16028 16029 16030 16031 16032
## [16033] 16033 16034 16035 16036 16037 16038 16039 16040 16041 16042 16043 16044
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## [16105] 16105 16106 16107 16108 16109 16110 16111 16112 16113 16114 16115 16116
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## [16129] 16129 16130 16131 16132 16133 16134 16135 16136 16137 16138 16139 16140
## [16141] 16141 16142 16143 16144 16145 16146 16147 16148 16149 16150 16151 16152
## [16153] 16153 16154 16155 16156 16157 16158 16159 16160 16161 16162 16163 16164
## [16165] 16165 16166 16167 16168 16169 16170 16171 16172 16173 16174 16175 16176
## [16177] 16177 16178 16179 16180 16181 16182 16183 16184 16185 16186 16187 16188
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## [16201] 16201 16202 16203 16204 16205 16206 16207 16208 16209 16210 16211 16212
## [16213] 16213 16214 16215 16216 16217 16218 16219 16220 16221 16222 16223 16224
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## [16285] 16285 16286 16287 16288 16289 16290 16291 16292 16293 16294 16295 16296
## [16297] 16297 16298 16299 16300 16301 16302 16303 16304 16305 16306 16307 16308
## [16309] 16309 16310 16311 16312 16313 16314 16315 16316 16317 16318 16319 16320
## [16321] 16321 16322 16323 16324 16325 16326 16327 16328 16329 16330 16331 16332
## [16333] 16333 16334 16335 16336 16337 16338 16339 16340 16341 16342 16343 16344
## [16345] 16345 16346 16347 16348 16349 16350 16351 16352 16353 16354 16355 16356
## [16357] 16357 16358 16359 16360 16361 16362 16363 16364 16365 16366 16367 16368
## [16369] 16369 16370 16371 16372 16373 16374 16375 16376 16377 16378 16379 16380
## [16381] 16381 16382 16383 16384 16385 16386 16387 16388 16389 16390 16391 16392
## [16393] 16393 16394 16395 16396 16397 16398 16399 16400 16401 16402 16403 16404
## [16405] 16405 16406 16407 16408 16409 16410 16411 16412 16413 16414 16415 16416
## [16417] 16417 16418 16419 16420 16421 16422 16423 16424 16425 16426 16427 16428
## [16429] 16429 16430 16431 16432 16433 16434 16435 16436 16437 16438 16439 16440
## [16441] 16441 16442 16443 16444 16445 16446 16447 16448 16449 16450 16451 16452
## [16453] 16453 16454 16455 16456 16457 16458 16459 16460 16461 16462 16463 16464
## [16465] 16465 16466 16467 16468 16469 16470 16471 16472 16473 16474 16475 16476
## [16477] 16477 16478 16479 16480 16481 16482 16483 16484 16485 16486 16487 16488
## [16489] 16489 16490 16491 16492 16493 16494 16495 16496 16497 16498 16499 16500
## [16501] 16501 16502 16503 16504 16505 16506 16507 16508 16509 16510 16511 16512
## [16513] 16513 16514 16515 16516 16517 16518 16519 16520 16521 16522 16523 16524
## [16525] 16525 16526 16527 16528 16529 16530 16531 16532 16533 16534 16535 16536
## [16537] 16537 16538 16539 16540 16541 16542 16543 16544 16545 16546 16547 16548
## [16549] 16549 16550 16551 16552 16553 16554 16555 16556 16557 16558 16559 16560
## [16561] 16561 16562 16563 16564 16565 16566 16567 16568 16569 16570 16571 16572
## [16573] 16573 16574 16575 16576 16577 16578 16579 16580 16581 16582 16583 16584
## [16585] 16585 16586 16587 16588 16589 16590 16591 16592 16593 16594 16595 16596
## [16597] 16597 16598 16599 16600 16601 16602 16603 16604 16605 16606 16607 16608
## [16609] 16609 16610 16611 16612 16613 16614 16615 16616 16617 16618 16619 16620
## [16621] 16621 16622 16623 16624 16625 16626 16627 16628 16629 16630 16631 16632
## [16633] 16633 16634 16635 16636 16637 16638 16639 16640 16641 16642 16643 16644
## [16645] 16645 16646 16647 16648 16649 16650 16651 16652 16653 16654 16655 16656
## [16657] 16657 16658 16659 16660 16661 16662 16663 16664 16665 16666 16667 16668
## [16669] 16669 16670 16671 16672 16673 16674 16675 16676 16677 16678 16679 16680
## [16681] 16681 16682 16683 16684 16685 16686 16687 16688 16689 16690 16691 16692
## [16693] 16693 16694 16695 16696 16697 16698 16699 16700 16701 16702 16703 16704
## [16705] 16705 16706 16707 16708 16709 16710 16711 16712 16713 16714 16715 16716
## [16717] 16717 16718 16719 16720 16721 16722 16723 16724 16725 16726 16727 16728
## [16729] 16729 16730 16731 16732 16733 16734 16735 16736 16737 16738 16739 16740
## [16741] 16741 16742 16743 16744 16745 16746 16747 16748 16749 16750 16751 16752
## [16753] 16753 16754 16755 16756 16757 16758 16759 16760 16761 16762 16763 16764
## [16765] 16765 16766 16767 16768 16769 16770 16771 16772 16773 16774 16775 16776
## [16777] 16777 16778 16779 16780 16781 16782 16783 16784 16785 16786 16787 16788
## [16789] 16789 16790 16791 16792 16793 16794 16795 16796 16797 16798 16799 16800
## [16801] 16801 16802 16803 16804 16805 16806 16807 16808 16809 16810 16811 16812
## [16813] 16813 16814 16815 16816 16817 16818 16819 16820 16821 16822 16823 16824
## [16825] 16825 16826 16827 16828 16829 16830 16831 16832 16833 16834 16835 16836
## [16837] 16837 16838 16839 16840 16841 16842 16843 16844 16845 16846 16847 16848
## [16849] 16849 16850 16851 16852 16853 16854 16855 16856 16857 16858 16859 16860
## [16861] 16861 16862 16863 16864 16865 16866 16867 16868 16869 16870 16871 16872
## [16873] 16873 16874 16875 16876 16877 16878 16879 16880 16881 16882 16883 16884
## [16885] 16885 16886 16887 16888 16889 16890 16891 16892 16893 16894 16895 16896
## [16897] 16897 16898 16899 16900 16901 16902 16903 16904 16905 16906 16907 16908
## [16909] 16909 16910 16911 16912 16913 16914 16915 16916 16917 16918 16919 16920
## [16921] 16921 16922 16923 16924 16925 16926 16927 16928 16929 16930 16931 16932
## [16933] 16933 16934 16935 16936 16937 16938 16939 16940 16941 16942 16943 16944
## [16945] 16945 16946 16947 16948 16949 16950 16951 16952 16953 16954 16955 16956
## [16957] 16957 16958 16959 16960 16961 16962 16963 16964 16965 16966 16967 16968
## [16969] 16969 16970 16971 16972 16973 16974 16975 16976 16977 16978 16979 16980
## [16981] 16981 16982 16983 16984 16985 16986 16987 16988 16989 16990 16991 16992
## [16993] 16993 16994 16995 16996 16997 16998 16999 17000 17001 17002 17003 17004
## [17005] 17005 17006 17007 17008 17009 17010 17011 17012 17013 17014 17015 17016
## [17017] 17017 17018 17019 17020 17021 17022 17023 17024 17025 17026 17027 17028
## [17029] 17029 17030 17031 17032 17033 17034 17035 17036 17037 17038 17039 17040
## [17041] 17041 17042 17043 17044 17045 17046 17047 17048 17049 17050 17051 17052
## [17053] 17053 17054 17055 17056 17057 17058 17059 17060 17061 17062 17063 17064
## [17065] 17065 17066 17067 17068 17069 17070 17071 17072 17073 17074 17075 17076
## [17077] 17077 17078 17079 17080 17081 17082 17083 17084 17085 17086 17087 17088
## [17089] 17089 17090 17091 17092 17093 17094 17095 17096 17097 17098 17099 17100
## [17101] 17101 17102 17103 17104 17105 17106 17107 17108 17109 17110 17111 17112
## [17113] 17113 17114 17115 17116 17117 17118 17119 17120 17121 17122 17123 17124
## [17125] 17125 17126 17127 17128 17129 17130 17131 17132 17133 17134 17135 17136
## [17137] 17137 17138 17139 17140 17141 17142 17143 17144 17145 17146 17147 17148
## [17149] 17149 17150 17151 17152 17153 17154 17155 17156 17157 17158 17159 17160
## [17161] 17161 17162 17163 17164 17165 17166 17167 17168 17169 17170 17171 17172
## [17173] 17173 17174 17175 17176 17177 17178 17179 17180 17181 17182 17183 17184
## [17185] 17185 17186 17187 17188 17189 17190 17191 17192 17193 17194 17195 17196
## [17197] 17197 17198 17199 17200 17201 17202 17203 17204 17205 17206 17207 17208
## [17209] 17209 17210 17211 17212 17213 17214 17215 17216 17217 17218 17219 17220
## [17221] 17221 17222 17223 17224 17225 17226 17227 17228 17229 17230 17231 17232
## [17233] 17233 17234 17235 17236 17237 17238 17239 17240 17241 17242 17243 17244
## [17245] 17245 17246 17247 17248 17249 17250 17251 17252 17253 17254 17255 17256
## [17257] 17257 17258 17259 17260 17261 17262 17263 17264 17265 17266 17267 17268
## [17269] 17269 17270 17271 17272 17273 17274 17275 17276 17277 17278 17279 17280
## [17281] 17281 17282 17283 17284 17285 17286 17287 17288 17289 17290 17291 17292
## [17293] 17293 17294 17295 17296 17297 17298 17299 17300 17301 17302 17303 17304
## [17305] 17305 17306 17307 17308 17309 17310 17311 17312 17313 17314 17315 17316
## [17317] 17317 17318 17319 17320 17321 17322 17323 17324 17325 17326 17327 17328
## [17329] 17329 17330 17331 17332 17333 17334 17335 17336 17337 17338 17339 17340
## [17341] 17341 17342 17343 17344 17345 17346 17347 17348 17349 17350 17351 17352
## [17353] 17353 17354 17355 17356 17357 17358 17359 17360 17361 17362 17363 17364
## [17365] 17365 17366 17367 17368 17369 17370 17371 17372 17373 17374 17375 17376
## [17377] 17377 17378 17379 17380 17381 17382 17383 17384 17385 17386 17387 17388
## [17389] 17389 17390 17391 17392 17393 17394 17395 17396 17397 17398 17399 17400
## [17401] 17401 17402 17403 17404 17405 17406 17407 17408 17409 17410 17411 17412
## [17413] 17413 17414 17415 17416 17417 17418 17419 17420 17421 17422 17423 17424
## [17425] 17425 17426 17427 17428 17429 17430 17431 17432 17433 17434 17435 17436
## [17437] 17437 17438 17439 17440 17441 17442 17443 17444 17445 17446 17447 17448
## [17449] 17449 17450 17451 17452 17453 17454 17455 17456 17457 17458 17459 17460
## [17461] 17461 17462 17463 17464 17465 17466 17467 17468 17469 17470 17471 17472
## [17473] 17473 17474 17475 17476 17477 17478 17479 17480 17481 17482 17483 17484
## [17485] 17485 17486 17487 17488 17489 17490 17491 17492 17493 17494 17495 17496
## [17497] 17497 17498 17499 17500 17501 17502 17503 17504 17505 17506 17507 17508
## [17509] 17509 17510 17511 17512 17513 17514 17515 17516 17517 17518 17519 17520
## [17521] 17521 17522 17523 17524 17525 17526 17527 17528 17529 17530 17531 17532
## [17533] 17533 17534 17535 17536 17537 17538 17539 17540 17541 17542 17543 17544
## [17545] 17545 17546 17547 17548 17549 17550 17551 17552 17553 17554 17555 17556
## [17557] 17557 17558 17559 17560 17561 17562 17563 17564 17565 17566 17567 17568
## [17569] 17569 17570 17571 17572 17573 17574 17575 17576 17577 17578 17579 17580
## [17581] 17581 17582 17583 17584 17585 17586 17587 17588 17589 17590 17591 17592
## [17593] 17593 17594 17595 17596 17597 17598 17599 17600 17601 17602 17603 17604
## [17605] 17605 17606 17607 17608 17609 17610 17611 17612 17613 17614 17615 17616
## [17617] 17617 17618 17619 17620 17621 17622 17623 17624 17625 17626 17627 17628
## [17629] 17629 17630 17631 17632 17633 17634 17635 17636 17637 17638 17639 17640
## [17641] 17641 17642 17643 17644 17645 17646 17647 17648 17649 17650 17651 17652
## [17653] 17653 17654 17655 17656 17657 17658 17659 17660 17661 17662 17663 17664
## [17665] 17665 17666 17667 17668 17669 17670 17671 17672 17673 17674 17675 17676
## [17677] 17677 17678 17679 17680 17681 17682 17683 17684 17685 17686 17687 17688
## [17689] 17689 17690 17691 17692 17693 17694 17695 17696 17697 17698 17699 17700
## [17701] 17701 17702 17703 17704 17705 17706 17707 17708 17709 17710 17711 17712
## [17713] 17713 17714 17715 17716 17717 17718 17719 17720 17721 17722 17723 17724
## [17725] 17725 17726 17727 17728 17729 17730 17731 17732 17733 17734 17735 17736
## [17737] 17737 17738 17739 17740 17741 17742 17743 17744 17745 17746 17747 17748
## [17749] 17749 17750 17751 17752 17753 17754 17755 17756 17757 17758 17759 17760
## [17761] 17761 17762 17763 17764 17765 17766 17767 17768 17769 17770 17771 17772
## [17773] 17773 17774 17775 17776 17777 17778 17779 17780 17781 17782 17783 17784
## [17785] 17785 17786 17787 17788 17789 17790 17791 17792 17793 17794 17795 17796
## [17797] 17797 17798 17799 17800 17801 17802 17803 17804 17805 17806 17807 17808
## [17809] 17809 17810 17811 17812 17813 17814 17815 17816 17817 17818 17819 17820
## [17821] 17821 17822 17823 17824 17825 17826 17827 17828 17829 17830 17831 17832
## [17833] 17833 17834 17835 17836 17837 17838 17839 17840 17841 17842 17843 17844
## [17845] 17845 17846 17847 17848 17849 17850 17851 17852 17853 17854 17855 17856
## [17857] 17857 17858 17859 17860 17861 17862 17863 17864 17865 17866 17867 17868
## [17869] 17869 17870 17871 17872 17873 17874 17875 17876 17877 17878 17879 17880
## [17881] 17881 17882 17883 17884 17885 17886 17887 17888 17889 17890 17891 17892
## [17893] 17893 17894 17895 17896 17897 17898 17899 17900 17901 17902 17903 17904
## [17905] 17905 17906 17907 17908 17909 17910 17911 17912 17913 17914 17915 17916
## [17917] 17917 17918 17919 17920 17921 17922 17923 17924 17925 17926 17927 17928
## [17929] 17929 17930 17931 17932 17933 17934 17935 17936 17937 17938 17939 17940
## [17941] 17941 17942 17943 17944 17945 17946 17947 17948 17949 17950 17951 17952
## [17953] 17953 17954 17955 17956 17957 17958 17959 17960 17961 17962 17963 17964
## [17965] 17965 17966 17967 17968 17969 17970 17971 17972 17973 17974 17975 17976
## [17977] 17977 17978 17979 17980 17981 17982 17983 17984 17985 17986 17987 17988
## [17989] 17989 17990 17991 17992 17993 17994 17995 17996 17997 17998 17999 18000
## [18001] 18001 18002 18003 18004 18005 18006 18007 18008 18009 18010 18011 18012
## [18013] 18013 18014 18015 18016 18017 18018 18019 18020 18021 18022 18023 18024
## [18025] 18025 18026 18027 18028 18029 18030 18031 18032 18033 18034 18035 18036
## [18037] 18037 18038 18039 18040 18041 18042 18043 18044 18045 18046 18047 18048
## [18049] 18049 18050 18051 18052 18053 18054 18055 18056 18057 18058 18059 18060
## [18061] 18061 18062 18063 18064 18065 18066 18067 18068 18069 18070 18071 18072
## [18073] 18073 18074 18075 18076 18077 18078 18079 18080 18081 18082 18083 18084
## [18085] 18085 18086 18087 18088 18089 18090 18091 18092 18093 18094 18095 18096
## [18097] 18097 18098 18099 18100 18101 18102 18103 18104 18105 18106 18107 18108
## [18109] 18109 18110 18111 18112 18113 18114 18115 18116

# Then, I correct country and variable names.

Government_Responses$COUNTRY <- Government_Responses$CountryName
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Cape Verde", "Cabo Verde", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Congo", "Congo (Brazzaville)", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Democratic Republic of Congo", "Congo (Kinshasa)", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Myanmar", "Burma", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Czech Republic", "Czechia", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Swaziland", "Eswatini", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Krygz Republic", "Kyrgyzstan", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "South Korea", "Korea, South", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Macedonia", "North Macedonia", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Slovak Republic", "Slovakia", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Macedonia", "North Macedonia", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Taiwan", "Taiwan*", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "Timor", "Timor-Leste", Government_Responses$COUNTRY)
Government_Responses$COUNTRY <- ifelse(Government_Responses$COUNTRY == "United States", "US", Government_Responses$COUNTRY)

colnames(Government_Responses)

##  [1] "CountryName"                          
##  [2] "CountryCode"                          
##  [3] "Date"                                 
##  [4] "C1_School closing"                    
##  [5] "C1_Flag"                              
##  [6] "C1_Notes"                             
##  [7] "C2_Workplace closing"                 
##  [8] "C2_Flag"                              
##  [9] "C2_Notes"                             
## [10] "C3_Cancel public events"              
## [11] "C3_Flag"                              
## [12] "C3_Notes"                             
## [13] "C4_Restrictions on gatherings"        
## [14] "C4_Flag"                              
## [15] "C4_Notes"                             
## [16] "C5_Close public transport"            
## [17] "C5_Flag"                              
## [18] "C5_Notes"                             
## [19] "C6_Stay at home requirements"         
## [20] "C6_Flag"                              
## [21] "C6_Notes"                             
## [22] "C7_Restrictions on internal movement" 
## [23] "C7_Flag"                              
## [24] "C7_Notes"                             
## [25] "C8_International travel controls"     
## [26] "C8_Notes"                             
## [27] "E1_Income support"                    
## [28] "E1_Flag"                              
## [29] "E1_Notes"                             
## [30] "E2_Debt/contract relief"              
## [31] "E2_Notes"                             
## [32] "E3_Fiscal measures"                   
## [33] "E3_Notes"                             
## [34] "E4_International support"             
## [35] "E4_Notes"                             
## [36] "H1_Public information campaigns"      
## [37] "H1_Flag"                              
## [38] "H1_Notes"                             
## [39] "H2_Testing policy"                    
## [40] "H2_Notes"                             
## [41] "H3_Contact tracing"                   
## [42] "H3_Notes"                             
## [43] "H4_Emergency investment in healthcare"
## [44] "H4_Notes"                             
## [45] "H5_Investment in vaccines"            
## [46] "H5_Notes"                             
## [47] "M1_Wildcard"                          
## [48] "M1_Notes"                             
## [49] "ConfirmedCases"                       
## [50] "ConfirmedDeaths"                      
## [51] "StringencyIndex"                      
## [52] "StringencyIndexForDisplay"            
## [53] "LegacyStringencyIndex"                
## [54] "LegacyStringencyIndexForDisplay"      
## [55] "COUNTRY"

Government_Responses$C1_School.closing <- Government_Responses$`C1_School closing`
Government_Responses$C2_Workplace.closing <- Government_Responses$`C2_Workplace closing`
Government_Responses$C3_Cancel.public.events <- Government_Responses$`C3_Cancel public events`
Government_Responses$C4_Restrictions.on.gatherings <- Government_Responses$`C4_Restrictions on gatherings`
Government_Responses$C5_Close.public.transport <- Government_Responses$`C5_Close public transport`
Government_Responses$C6_Stay.at.home.requirements <- Government_Responses$`C6_Stay at home requirements`
Government_Responses$C7_Restrictions.on.internal.movement <- Government_Responses$`C7_Restrictions on internal movement`
Government_Responses$C8_International.travel.controls <- Government_Responses$`C8_International travel controls`
Government_Responses$E1_Income.support <- Government_Responses$`E1_Income support`
Government_Responses$E2_Debt.contract.relief <- Government_Responses$`E2_Debt/contract relief`
Government_Responses$E3_Fiscal.measures <- Government_Responses$`E3_Fiscal measures`
Government_Responses$E4_International.support <- Government_Responses$`E4_International support`
Government_Responses$H1_Public.information.campaigns <- Government_Responses$`H1_Public information campaigns`
Government_Responses$H2_Testing.policy <- Government_Responses$`H2_Testing policy`
Government_Responses$H3_Contact.tracing <- Government_Responses$`H3_Contact tracing`
Government_Responses$H4_Emergency.investment.in.healthcare <- Government_Responses$`H4_Emergency investment in healthcare`
Government_Responses$H5_Investment.in.vaccines <- Government_Responses$`H5_Investment in vaccines`

# I now subset the data. PLEASE NOTE the observations change in this merge. Oxford began collecting data before Hopkins started tracking cases and deaths. For simplicity, I use the merged data from before to visualize the confirmed cases and deaths (as these dates are filtered correctly).
Government_Responses <- subset(Government_Responses, is.element(Government_Responses$COUNTRY, Temporary_Data_Country$COUNTRY))

# Note that we have two sets of variables for confirmed cases and deaths. The first, I label "Oxford_Cases" and "Oxford_Deaths". The second (from John Hopkins) I label "Total_Cases_Country" and "Total_Deceased_Country". At a later date, we might want to cross-reference these values. Note that Oxford does not provide calculations for province/state; however it is highly likely they used the (same) John Hopkins data.
Government_Responses$Oxford_Cases <- Government_Responses$ConfirmedCases
Government_Responses$Oxford_Deaths <- Government_Responses$ConfirmedDeaths

# Once more, I only keep the variables we are interested in. I elect not to keep the notes... these can be re-added if preferred. E4_International.support also has almost no values above 0, so it is dropped. 

colnames(Government_Responses)

##  [1] "CountryName"                          
##  [2] "CountryCode"                          
##  [3] "Date"                                 
##  [4] "C1_School closing"                    
##  [5] "C1_Flag"                              
##  [6] "C1_Notes"                             
##  [7] "C2_Workplace closing"                 
##  [8] "C2_Flag"                              
##  [9] "C2_Notes"                             
## [10] "C3_Cancel public events"              
## [11] "C3_Flag"                              
## [12] "C3_Notes"                             
## [13] "C4_Restrictions on gatherings"        
## [14] "C4_Flag"                              
## [15] "C4_Notes"                             
## [16] "C5_Close public transport"            
## [17] "C5_Flag"                              
## [18] "C5_Notes"                             
## [19] "C6_Stay at home requirements"         
## [20] "C6_Flag"                              
## [21] "C6_Notes"                             
## [22] "C7_Restrictions on internal movement" 
## [23] "C7_Flag"                              
## [24] "C7_Notes"                             
## [25] "C8_International travel controls"     
## [26] "C8_Notes"                             
## [27] "E1_Income support"                    
## [28] "E1_Flag"                              
## [29] "E1_Notes"                             
## [30] "E2_Debt/contract relief"              
## [31] "E2_Notes"                             
## [32] "E3_Fiscal measures"                   
## [33] "E3_Notes"                             
## [34] "E4_International support"             
## [35] "E4_Notes"                             
## [36] "H1_Public information campaigns"      
## [37] "H1_Flag"                              
## [38] "H1_Notes"                             
## [39] "H2_Testing policy"                    
## [40] "H2_Notes"                             
## [41] "H3_Contact tracing"                   
## [42] "H3_Notes"                             
## [43] "H4_Emergency investment in healthcare"
## [44] "H4_Notes"                             
## [45] "H5_Investment in vaccines"            
## [46] "H5_Notes"                             
## [47] "M1_Wildcard"                          
## [48] "M1_Notes"                             
## [49] "ConfirmedCases"                       
## [50] "ConfirmedDeaths"                      
## [51] "StringencyIndex"                      
## [52] "StringencyIndexForDisplay"            
## [53] "LegacyStringencyIndex"                
## [54] "LegacyStringencyIndexForDisplay"      
## [55] "COUNTRY"                              
## [56] "C1_School.closing"                    
## [57] "C2_Workplace.closing"                 
## [58] "C3_Cancel.public.events"              
## [59] "C4_Restrictions.on.gatherings"        
## [60] "C5_Close.public.transport"            
## [61] "C6_Stay.at.home.requirements"         
## [62] "C7_Restrictions.on.internal.movement" 
## [63] "C8_International.travel.controls"     
## [64] "E1_Income.support"                    
## [65] "E2_Debt.contract.relief"              
## [66] "E3_Fiscal.measures"                   
## [67] "E4_International.support"             
## [68] "H1_Public.information.campaigns"      
## [69] "H2_Testing.policy"                    
## [70] "H3_Contact.tracing"                   
## [71] "H4_Emergency.investment.in.healthcare"
## [72] "H5_Investment.in.vaccines"            
## [73] "Oxford_Cases"                         
## [74] "Oxford_Deaths"

myvarsgovernment <- c("COUNTRY", "Date", "C1_School.closing", "C1_Flag", "C2_Workplace.closing", "C2_Flag", "C3_Cancel.public.events", "C3_Flag", "C4_Restrictions.on.gatherings", "C4_Flag", "C5_Close.public.transport", "C5_Flag", "C6_Stay.at.home.requirements", "C6_Flag", "C7_Restrictions.on.internal.movement", "C7_Flag", "C8_International.travel.controls", "E1_Income.support", "E1_Flag", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H1_Public.information.campaigns", "H1_Flag", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare", "H5_Investment.in.vaccines", "M1_Wildcard", "StringencyIndex", "LegacyStringencyIndex", "Oxford_Cases", "Oxford_Deaths")

Government_Responses <- Government_Responses[myvarsgovernment]

# I then merge our data.
Final_Data_Country <- merge(Temporary_Data_Country, Government_Responses, by = c("COUNTRY", "Date"), all=TRUE)

# Next, I specify a set of lagged government response variables for use in our regression later (one-week, two-week, and three-week lags). First, I filter the data and create a lag function for each week (these functions are also used for generating the lags in our testing data).

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# This chunk of code is quite extensive. In our regressions, we use the "lag" function to generate lags for our key variables, rather than using the generated lagged variables below. I simply include them in order to visualize the dataframe with lags incorporated (as the lag function will simply lag existing variables in analysis; not generate new ones to explore). Using this function also allows us to observe which nations do not have enough observations to generate specified lags.

# One Week
Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C1_School.closing", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_School_Closing_One_Week <- as.factor(Final_Data_Country$Lagged_School_Closing_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C1_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_General_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_School_Closing_General_One_Week <- as.factor(Final_Data_Country$Lagged_School_Closing_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C2_Workplace.closing", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Workplace_Closing_One_Week <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C2_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_General_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Workplace_Closing_General_One_Week <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C3_Cancel.public.events", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Events_One_Week <- as.factor(Final_Data_Country$Lagged_Public_Events_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C3_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_General_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Events_General_One_Week <- as.factor(Final_Data_Country$Lagged_Public_Events_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C4_Restrictions.on.gatherings", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Gatherings_One_Week <- as.factor(Final_Data_Country$Lagged_Gatherings_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C4_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_General_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Gatherings_General_One_Week <- as.factor(Final_Data_Country$Lagged_Gatherings_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C5_Close.public.transport", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Transport_One_Week <- as.factor(Final_Data_Country$Lagged_Public_Transport_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C5_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_General_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Transport_General_One_Week <- as.factor(Final_Data_Country$Lagged_Public_Transport_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C6_Stay.at.home.requirements", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Lockdown_One_Week <- as.factor(Final_Data_Country$Lagged_Lockdown_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C6_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_General_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Lockdown_General_One_Week <- as.factor(Final_Data_Country$Lagged_Lockdown_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C7_Restrictions.on.internal.movement", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_One_Week", keepInvalid =   
  TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Internal_Movement_One_Week <- as.factor(Final_Data_Country$Lagged_Internal_Movement_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C7_Flag",slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_General_One_Week", keepInvalid = TRUE, reminder =  FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Internal_Movement_General_One_Week <- as.factor(Final_Data_Country$Lagged_Internal_Movement_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C8_International.travel.controls", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_International_Travel_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_International_Travel_One_Week <- as.factor(Final_Data_Country$Lagged_International_Travel_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E1_Income.support", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_One_Week", keepInvalid =   
  TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Income_Support_One_Week <- as.factor(Final_Data_Country$Lagged_Income_Support_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E1_Flag",slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_General_One_Week", keepInvalid = TRUE, reminder =  FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Income_Support_General_One_Week <- as.factor(Final_Data_Country$Lagged_Income_Support_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E2_Debt.contract.relief", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Debt_Relief_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Debt_Relief_One_Week <- as.factor(Final_Data_Country$Lagged_Debt_Relief_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E3_Fiscal.measures", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Fiscal_Measures_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H1_Public.information.campaigns", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Campaign_One_Week <- as.factor(Final_Data_Country$Lagged_Campaign_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H1_Flag", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_General_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Campaign_General_One_Week <- as.factor(Final_Data_Country$Lagged_Campaign_General_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H2_Testing.policy", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Testing_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Testing_One_Week <- as.factor(Final_Data_Country$Lagged_Testing_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H3_Contact.tracing", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Tracing_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Tracing_One_Week <- as.factor(Final_Data_Country$Lagged_Tracing_One_Week)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H4_Emergency.investment.in.healthcare", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H5_Investment.in.vaccines", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_One_Week", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "StringencyIndex", TimeVar = "Date", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Stringency_Index_One_Week", keepInvalid 
  = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "LegacyStringencyIndex", TimeVar = "Date", slideBy = -7, GroupVar = "COUNTRY", NewVar = "Lagged_Legacy_Stringency_Index_One_Week", keepInvalid 
  = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 6 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

# Two Weeks
Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C1_School.closing", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_School_Closing_Two_Weeks <- as.factor(Final_Data_Country$Lagged_School_Closing_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C1_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_School_Closing_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_School_Closing_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C2_Workplace.closing", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Workplace_Closing_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C2_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Workplace_Closing_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C3_Cancel.public.events", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Events_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Events_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C3_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Events_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Events_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C4_Restrictions.on.gatherings", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Gatherings_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Gatherings_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C4_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Gatherings_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Gatherings_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C5_Close.public.transport", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Transport_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Transport_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C5_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Transport_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Transport_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C6_Stay.at.home.requirements", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Lockdown_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Lockdown_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C6_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Lockdown_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Lockdown_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C7_Restrictions.on.internal.movement", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_Two_Weeks", keepInvalid =   
  TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Internal_Movement_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Internal_Movement_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C7_Flag",slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_General_Two_Weeks", keepInvalid = TRUE, reminder =  FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Internal_Movement_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Internal_Movement_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C8_International.travel.controls", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_International_Travel_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_International_Travel_Two_Weeks <- as.factor(Final_Data_Country$Lagged_International_Travel_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E1_Income.support", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_Two_Weeks", keepInvalid =   
  TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Income_Support_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Income_Support_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E1_Flag",slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_General_Two_Weeks", keepInvalid = TRUE, reminder =  FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Income_Support_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Income_Support_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E2_Debt.contract.relief", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Debt_Relief_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Debt_Relief_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Debt_Relief_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E3_Fiscal.measures", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Fiscal_Measures_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H1_Public.information.campaigns", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Campaign_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Campaign_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H1_Flag", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_General_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Campaign_General_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Campaign_General_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H2_Testing.policy", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Testing_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Testing_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Testing_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H3_Contact.tracing", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Tracing_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Tracing_Two_Weeks <- as.factor(Final_Data_Country$Lagged_Tracing_Two_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H4_Emergency.investment.in.healthcare", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H5_Investment.in.vaccines", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_Two_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "StringencyIndex", TimeVar = "Date", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Stringency_Index_Two_Weeks", keepInvalid 
  = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "LegacyStringencyIndex", TimeVar = "Date", slideBy = -14, GroupVar = "COUNTRY", NewVar = "Lagged_Legacy_Stringency_Index_Two_Weeks", keepInvalid 
  = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 13 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

# Three Weeks
Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C1_School.closing", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_School_Closing_Three_Weeks <- as.factor(Final_Data_Country$Lagged_School_Closing_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C1_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_School_Closing_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_School_Closing_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_School_Closing_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C2_Workplace.closing", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Workplace_Closing_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C2_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Workplace_Closing_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Workplace_Closing_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Workplace_Closing_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C3_Cancel.public.events", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Events_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Events_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C3_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Events_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Events_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Events_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C4_Restrictions.on.gatherings", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Gatherings_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Gatherings_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C4_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Gatherings_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Gatherings_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Gatherings_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C5_Close.public.transport", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Transport_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Transport_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C5_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Public_Transport_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Public_Transport_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Public_Transport_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C6_Stay.at.home.requirements", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Lockdown_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Lockdown_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C6_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Lockdown_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Lockdown_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Lockdown_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C7_Restrictions.on.internal.movement", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_Three_Weeks", keepInvalid =   
  TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Internal_Movement_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Internal_Movement_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C7_Flag",slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Internal_Movement_General_Three_Weeks", keepInvalid = TRUE, reminder =  FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Internal_Movement_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Internal_Movement_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "C8_International.travel.controls", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_International_Travel_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_International_Travel_Three_Weeks <- as.factor(Final_Data_Country$Lagged_International_Travel_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E1_Income.support", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_Three_Weeks", keepInvalid =   
  TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Income_Support_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Income_Support_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E1_Flag",slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Income_Support_General_Three_Weeks", keepInvalid = TRUE, reminder =  FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Income_Support_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Income_Support_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E2_Debt.contract.relief", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Debt_Relief_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Debt_Relief_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Debt_Relief_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "E3_Fiscal.measures", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Fiscal_Measures_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H1_Public.information.campaigns", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Campaign_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Campaign_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H1_Flag", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Campaign_General_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Campaign_General_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Campaign_General_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H2_Testing.policy", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Testing_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Testing_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Testing_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H3_Contact.tracing", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Tracing_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country$Lagged_Tracing_Three_Weeks <- as.factor(Final_Data_Country$Lagged_Tracing_Three_Weeks)

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H4_Emergency.investment.in.healthcare", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "H5_Investment.in.vaccines", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Health_Care_Three_Weeks", keepInvalid = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "StringencyIndex", TimeVar = "Date", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Stringency_Index_Three_Weeks", keepInvalid 
  = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Final_Data_Country <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  slide(Var = "LegacyStringencyIndex", TimeVar = "Date", slideBy = -21, GroupVar = "COUNTRY", NewVar = "Lagged_Legacy_Stringency_Index_Three_Weeks", keepInvalid 
  = TRUE, reminder = FALSE)

## Converting to plain data frame from tbl_df.

## 
## Warning: the following groups have 20 or fewer observations.
##   No valid lag/lead can be created.
##   NA will be returned for these observations in the new lag/lead variable.
##   They will be returned at the bottom of the data frame.

## American Samoa
## Anguilla
## Antarctica
## Bouvet Island
## British Indian Ocean Territory
## British Virgin Islands
## Cayman Islands
## Central African Republic
## Christmas Island
## Cocos [Keeling] Islands
## Comoros
## Cook Islands
## Falkland Islands [Islas Malvinas]
## Faroe Islands
## French Guiana
## French Polynesia
## French Southern Territories
## Gaza Strip
## Gibraltar
## Guadeloupe
## Guernsey
## Heard Island and McDonald Islands
## Isle of Man
## Jersey
## Kiribati
## Macau
## Marshall Islands
## Martinique
## Mayotte
## Micronesia
## Montserrat
## Nauru
## Netherlands Antilles
## New Caledonia
## Niue
## Norfolk Island
## North Korea
## Northern Mariana Islands
## Palau
## Palestinian Territories
## Pitcairn Islands
## Réunion
## Saint Helena
## Saint Pierre and Miquelon
## Samoa
## Solomon Islands
## South Georgia and the South Sandwich Islands
## Svalbard and Jan Mayen
## Tajikistan
## Tokelau
## Tonga
## Turkmenistan
## Turks and Caicos Islands
## Tuvalu
## U.S. Minor Outlying Islands
## U.S. Virgin Islands
## Vanuatu
## Wallis and Futuna
## Western Sahara

## 

Here, I add coordinates for each nation in the dataset.

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# I am using the same Google coordinate data from: https://developers.google.com/public-data/docs/canonical/countries_csv. Note that I edited country names to match the data in the CSV itself (I only kept coordinates for nations in our dataframe). 
Country_Coordinates <- read.csv("data/Country_Coordinates.csv")

Country_Coordinates$COUNTRY <- Country_Coordinates$Country

colnames(Country_Coordinates)

## [1] "Latitude"  "Longitude" "Country"   "COUNTRY"

myvarscoords <- c("COUNTRY", "Latitude", "Longitude")
Country_Coordinates <- Country_Coordinates[myvarscoords]

# I subset the file to only include the nations in our final data.
Country_Coordinates <- subset(Country_Coordinates, is.element(Country_Coordinates$COUNTRY, Final_Data_Country$COUNTRY))

# Lastly, I merge the two dataframes.
Final_Data_Country <- merge(Final_Data_Country, Country_Coordinates, by = c("COUNTRY"), all=TRUE)

Now, I add socioeconomic controls from the World Development Indicators.

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# First, I add the proportion of the national population above 65 from: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS. 
pop65yo <- read_csv("data/pop65yo.csv")

## Parsed with column specification:
## cols(
##   value = col_double(),
##   country = col_character()
## )

pop65yo$COUNTRY <- pop65yo$country
pop65yo$prop65 <- pop65yo$value

# Next, I correct the country names.
pop65yo[19, "COUNTRY"] <- "Bahamas"
pop65yo[26, "COUNTRY"] <- "Brunei"
pop65yo[37, "COUNTRY"] <- "Congo (Kinshasa)"
pop65yo[38, "COUNTRY"] <- "Congo (Brazzaville)"
pop65yo[46, "COUNTRY"] <- "Czechia"
pop65yo[53, "COUNTRY"] <- "Egypt"
pop65yo[66, "COUNTRY"] <- "Gambia"
pop65yo[82, "COUNTRY"] <- "Iran"
pop65yo[95, "COUNTRY"] <- "Korea, South"
pop65yo[92, "COUNTRY"] <- "Kyrgyzstan"
pop65yo[97, "COUNTRY"] <- "Laos"
pop65yo[101, "COUNTRY"] <- "Saint Lucia"
pop65yo[116, "COUNTRY"] <- "Burma"
pop65yo[148, "COUNTRY"] <- "Russia"
pop65yo[162, "COUNTRY"] <- "Slovakia"
pop65yo[167, "COUNTRY"] <- "Syria"
pop65yo[182, "COUNTRY"] <- "US"
pop65yo[184, "COUNTRY"] <- "Saint Vincent and the Grenadines"
pop65yo[185, "COUNTRY"] <- "Venezuela"
pop65yo[186, "COUNTRY"] <- "Virgin Islands"
pop65yo[190, "COUNTRY"] <- "Yemen"
pop65yo[5, "COUNTRY"] <- "United Arab Emirates"
pop65yo[62, "COUNTRY"] <- "United Kingdom"
pop65yo[132, "COUNTRY"] <- "New Zealand"
pop65yo[150, "COUNTRY"] <- "Saudi Arabia"
pop65yo[191, "COUNTRY"] <- "South Africa"

pop65yo <- subset(pop65yo, is.element(pop65yo$COUNTRY, Final_Data_Country$COUNTRY))

# I only keep the columns I care about.
colnames(pop65yo)

## [1] "value"   "country" "COUNTRY" "prop65"

myvarspop65yo <- c("prop65", "COUNTRY")

pop65yo <- pop65yo[myvarspop65yo]

pop65yo <- pop65yo[order(pop65yo$COUNTRY, pop65yo$prop65),]

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

Final_Data_Country <- merge(Final_Data_Country, pop65yo, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# Now, I add another world development indicator for the proportion of the population which is urban: https://data.worldbank.org/indicator/SP.URB.TOTL.in.zs. 
urbanpp <- read_csv("data/urbanpp.csv")

## Parsed with column specification:
## cols(
##   propurban = col_double(),
##   COUNTRY = col_character()
## )

# Next, I correct the country names.
urbanpp[21, "COUNTRY"] <- "Bahamas"
urbanpp[29, "COUNTRY"] <- "Brunei"
urbanpp[40, "COUNTRY"] <- "Congo (Kinshasa)"
urbanpp[41, "COUNTRY"] <- "Congo (Brazzaville)"
urbanpp[50, "COUNTRY"] <- "Czechia"
urbanpp[58, "COUNTRY"] <- "Egypt"
urbanpp[73, "COUNTRY"] <- "Gambia"
urbanpp[91, "COUNTRY"] <- "Iran"
urbanpp[105, "COUNTRY"] <- "Korea, South"
urbanpp[101, "COUNTRY"] <- "Kyrgyzstan"
urbanpp[107, "COUNTRY"] <- "Laos"
urbanpp[111, "COUNTRY"] <- "Saint Lucia"
urbanpp[129, "COUNTRY"] <- "Burma"
urbanpp[164, "COUNTRY"] <- "Russia"
urbanpp[179, "COUNTRY"] <- "Slovakia"
urbanpp[185, "COUNTRY"] <- "Syria"
urbanpp[202, "COUNTRY"] <- "US"
urbanpp[204, "COUNTRY"] <- "Saint Vincent and the Grenadines"
urbanpp[205, "COUNTRY"] <- "Venezuela"
urbanpp[206, "COUNTRY"] <- "Virgin Islands"
urbanpp[211, "COUNTRY"] <- "Yemen"
urbanpp[6, "COUNTRY"] <- "United Arab Emirates"
urbanpp[68, "COUNTRY"] <- "United Kingdom"
urbanpp[147, "COUNTRY"] <- "New Zealand"
urbanpp[166, "COUNTRY"] <- "Saudi Arabia"
urbanpp[212, "COUNTRY"] <- "South Africa"

urbanpp <- subset(urbanpp, is.element(urbanpp$COUNTRY, Final_Data_Country$COUNTRY))

# I only keep the columns I care about.
colnames(urbanpp)

## [1] "propurban" "COUNTRY"

myvarsurbanpp <- c("propurban", "COUNTRY")

urbanpp <- urbanpp[myvarsurbanpp]

Final_Data_Country <- merge(Final_Data_Country, urbanpp, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# Now, I add the population density indicator. Note the latest year available for this data is 2018. 
popdensity <- read.csv("data/popdensity.csv")
  
popdensity$COUNTRY <- popdensity$Country.Name

# Next, I correct the country names.
popdensity[22, "COUNTRY"] <- "Bahamas"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated

popdensity[30, "COUNTRY"] <- "Brunei"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated

popdensity[42, "COUNTRY"] <- "Congo (Kinshasa)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated

popdensity[43, "COUNTRY"] <- "Congo (Brazzaville)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated

popdensity[53, "COUNTRY"] <- "Czechia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated

popdensity[66, "COUNTRY"] <- "Egypt"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated

popdensity[85, "COUNTRY"] <- "Gambia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated

popdensity[111, "COUNTRY"] <- "Iran"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated

popdensity[125, "COUNTRY"] <- "Korea, South"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated

popdensity[121, "COUNTRY"] <- "Kyrgyzstan"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated

popdensity[128, "COUNTRY"] <- "Laos"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated

popdensity[132, "COUNTRY"] <- "Saint Lucia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated

popdensity[159, "COUNTRY"] <- "Burma"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated

popdensity[201, "COUNTRY"] <- "Russia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated

popdensity[220, "COUNTRY"] <- "Slovakia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated

popdensity[226, "COUNTRY"] <- "Syria"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated

popdensity[250, "COUNTRY"] <- "US"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated

popdensity[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated

popdensity[253, "COUNTRY"] <- "Venezuela"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated

popdensity[255, "COUNTRY"] <- "Virgin Islands"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated

popdensity[261, "COUNTRY"] <- "Yemen"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated

popdensity[7, "COUNTRY"] <- "United Arab Emirates"
popdensity[80, "COUNTRY"] <- "United Kingdom"
popdensity[179, "COUNTRY"] <- "New Zealand"
popdensity[204, "COUNTRY"] <- "Saudi Arabia"
popdensity[262, "COUNTRY"] <- "South Africa"

popdensity <- subset(popdensity, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(popdensity)

## [1] "Country.Name" "popdensity"   "COUNTRY"

myvarspopdensity <- c("popdensity", "COUNTRY")

popdensity <- popdensity[myvarspopdensity]

popdensity <- popdensity[order(popdensity$COUNTRY, popdensity$popdensity),]

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

Final_Data_Country <- merge(Final_Data_Country, popdensity, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]  

Merging the testing data.

***** This has been commented out for now, because of the poor quality of the data. However, the dataframe is updated with the newest results each time the chunk is run. If we want to re-implement testing in some way, we can uncomment and run this chunk. *****

# Next, I add our testing data from:
# owurl = "https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/testing/covid-testing-all-observations.csv"
# testcap <- read_csv(url(owurl))
# testcap <- separate(testcap, Entity, into=c("Country", "Unit type"), sep = " - ") 
# write_csv(testcap, path = "/Users/sameernair-desai/Desktop/data/testcap.csv")

# testcap$COUNTRY <- testcap$Country
# testcap$COUNTRY <- ifelse(testcap$COUNTRY == "Czech Republic", "Czechia", testcap$COUNTRY)
# testcap$COUNTRY <- ifelse(testcap$COUNTRY == "South Korea", "Korea, South", testcap$COUNTRY)
# testcap$COUNTRY <- ifelse(testcap$COUNTRY == "Taiwan", "Taiwan*", testcap$COUNTRY)
# testcap$COUNTRY <- ifelse(testcap$COUNTRY == "United States", "US", testcap$COUNTRY)
 
# testcap <- subset(testcap, is.element(testcap$COUNTRY, Final_Data_Country$COUNTRY))

# I don't like some of the variable names in the original data, so I change them below.
# testcap$Test_Type <- testcap$`Unit type`
# testcap$Cumulative_Tests <- testcap$`Cumulative total`
# testcap$Daily_Change_Cumulative_Tests <- testcap$`Daily change in cumulative total`
# testcap$Cumulative_Tests_Per_Thousand <- testcap$`Cumulative total per thousand`
# testcap$Daily_Change_Cumulative_Tests_Per_Thousand <- testcap$`Daily change in cumulative total per thousand`
# testcap$Three_Day_Rolling_Mean_Tests <- testcap$`3-day rolling mean daily change`
# testcap$Three_Day_Rolling_Mean_Tests_Per_Thousand <- testcap$`3-day rolling mean daily change per thousand`
# 
# Here, I keep the variables I care about.
# colnames(testcap)
# myvarstest <- c("COUNTRY", "Date", "Test_Type", "Cumulative_Tests", "Daily_Change_Cumulative_Tests", "Cumulative_Tests_Per_Thousand", "Daily_Change_Cumulative_Tests_Per_Thousand", "Three_Day_Rolling_Mean_Tests", "Three_Day_Rolling_Mean_Tests_Per_Thousand")

# testcap <- testcap[myvarstest]
 
# Now, I convert from long to wide format.
# testcap <- pivot_wider(testcap, id_cols = c("COUNTRY", "Date", "Test_Type"), names_from = "Test_Type", values_from = c("Cumulative_Tests", "Daily_Change_Cumulative_Tests", "Cumulative_Tests_Per_Thousand", "Daily_Change_Cumulative_Tests_Per_Thousand", "Three_Day_Rolling_Mean_Tests", "Three_Day_Rolling_Mean_Tests_Per_Thousand"))

# Now, I merge the data to our final file.
# Final_Data_Country <- merge(Final_Data_Country, testcap, by = c("COUNTRY", "Date"), all=TRUE)

Merging the mobility data.

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# Here, I integrate the Apple Mobility data. 

mobility <- read.csv("data/applemobilitytrends-2020-04-27.csv")

# First, we convert from wide to long format.
mobility <- pivot_longer(mobility, cols = starts_with("X"), names_to = "Dates")

# Next, we reformat the dates.
mobility$Dates<-sub(".", "", mobility$Dates)
mobility$Date <- mdy(mobility$Dates, quiet = FALSE, tz = NULL, locale = Sys.getlocale("LC_TIME"),
  truncated = 0)

# I only keep the variables we care about. I also filter for only countries (we can re-add cities at a later period).

mobility <- mobility %>%
  dplyr:: filter(mobility$geo_type == "country/region")

colnames(mobility)

## [1] "geo_type"            "region"              "transportation_type"
## [4] "alternative_name"    "Dates"               "value"              
## [7] "Date"

myvarsmobility <- c("region", "transportation_type", "Date", "value")

mobility <- mobility[myvarsmobility]

mobility$COUNTRY <- mobility$region

# Now, I re-convert from long to wide format.
mobility <- pivot_wider(mobility, id_cols = c("COUNTRY", "Date", "transportation_type"), names_from = "transportation_type", values_from = c("value"))

# Next, we ensure the countries from the mobility file and our data align.

mobility$COUNTRY <- ifelse(mobility$COUNTRY == "Czech Republic", "Czechia", mobility$COUNTRY)
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "Republic of Korea", "Korea, South", mobility$COUNTRY)
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "Taiwan", "Taiwan*", mobility$COUNTRY)
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "UK", "United Kingdom", mobility$COUNTRY)
mobility$COUNTRY <- ifelse(mobility$COUNTRY == "United States", "US", mobility$COUNTRY)

mobility <- subset(mobility, is.element(mobility$COUNTRY, Final_Data_Country$COUNTRY))

Final_Data_Country <- merge(Final_Data_Country, mobility, by = c("COUNTRY", "Date"), all=TRUE)

Now, I add our controls for cross-country travel.

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# First, I add information on the number of tourist arrivals (2018 is l.y.a.). Run this in the console
arrivals <- read.csv("data/arrivals.csv")

arrivals$COUNTRY <- arrivals$Country.Name

# Next, I correct the country names.
arrivals[22, "COUNTRY"] <- "Bahamas"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated

arrivals[30, "COUNTRY"] <- "Brunei"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated

arrivals[42, "COUNTRY"] <- "Congo (Kinshasa)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated

arrivals[43, "COUNTRY"] <- "Congo (Brazzaville)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated

arrivals[53, "COUNTRY"] <- "Czechia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated

arrivals[66, "COUNTRY"] <- "Egypt"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated

arrivals[85, "COUNTRY"] <- "Gambia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated

arrivals[111, "COUNTRY"] <- "Iran"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated

arrivals[125, "COUNTRY"] <- "Korea, South"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated

arrivals[121, "COUNTRY"] <- "Kyrgyzstan"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated

arrivals[128, "COUNTRY"] <- "Laos"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated

arrivals[132, "COUNTRY"] <- "Saint Lucia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated

arrivals[159, "COUNTRY"] <- "Burma"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated

arrivals[201, "COUNTRY"] <- "Russia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated

arrivals[220, "COUNTRY"] <- "Slovakia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated

arrivals[226, "COUNTRY"] <- "Syria"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated

arrivals[250, "COUNTRY"] <- "US"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated

arrivals[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated

arrivals[253, "COUNTRY"] <- "Venezuela"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated

arrivals[255, "COUNTRY"] <- "Virgin Islands"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated

arrivals[261, "COUNTRY"] <- "Yemen"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated

arrivals[7, "COUNTRY"] <- "United Arab Emirates"
arrivals[80, "COUNTRY"] <- "United Kingdom"
arrivals[179, "COUNTRY"] <- "New Zealand"
arrivals[204, "COUNTRY"] <- "Saudi Arabia"
arrivals[262, "COUNTRY"] <- "South Africa"

arrivals <- subset(arrivals, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(arrivals)

## [1] "Country.Name" "Country.Code" "arrivals"     "COUNTRY"

myvarsarrivals <- c("arrivals", "COUNTRY")

arrivals <- arrivals[myvarsarrivals]

Final_Data_Country <- merge(Final_Data_Country, arrivals, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# Next, I add information on tourism departures (2018 is l.y.a.). 
departures <- read.csv("data/departures.csv")

departures$COUNTRY <- departures$Country.Name

# Next, I correct the country names.
departures[22, "COUNTRY"] <- "Bahamas"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated

departures[30, "COUNTRY"] <- "Brunei"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated

departures[42, "COUNTRY"] <- "Congo (Kinshasa)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated

departures[43, "COUNTRY"] <- "Congo (Brazzaville)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated

departures[53, "COUNTRY"] <- "Czechia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated

departures[66, "COUNTRY"] <- "Egypt"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated

departures[85, "COUNTRY"] <- "Gambia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated

departures[111, "COUNTRY"] <- "Iran"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated

departures[125, "COUNTRY"] <- "Korea, South"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated

departures[121, "COUNTRY"] <- "Kyrgyzstan"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated

departures[128, "COUNTRY"] <- "Laos"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated

departures[132, "COUNTRY"] <- "Saint Lucia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated

departures[159, "COUNTRY"] <- "Burma"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated

departures[201, "COUNTRY"] <- "Russia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated

departures[220, "COUNTRY"] <- "Slovakia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated

departures[226, "COUNTRY"] <- "Syria"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated

departures[250, "COUNTRY"] <- "US"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated

departures[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated

departures[253, "COUNTRY"] <- "Venezuela"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated

departures[255, "COUNTRY"] <- "Virgin Islands"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated

departures[261, "COUNTRY"] <- "Yemen"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated

departures[7, "COUNTRY"] <- "United Arab Emirates"
departures[80, "COUNTRY"] <- "United Kingdom"
departures[179, "COUNTRY"] <- "New Zealand"
departures[204, "COUNTRY"] <- "Saudi Arabia"
departures[262, "COUNTRY"] <- "South Africa"

departures <- subset(departures, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(departures)

## [1] "Country.Name" "departures"   "COUNTRY"

myvarsdepartures <- c("departures", "COUNTRY")

departures <- departures[myvarsdepartures]

Final_Data_Country <- merge(Final_Data_Country, departures, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

Now, I add additional country-specific characteristics gathered from the World Bank. All data is from 2018, unless noted.

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# First, I add the percentage of vulnerable employment by nation from: https://data.worldbank.org/indicator/SL.EMP.VULN.ZS?view=chart 
vulnerable_employment <- read.csv("data/vulnerable_employment.csv")

# Next, I correct the country names.
vulnerable_employment[22, "COUNTRY"] <- "Bahamas"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated

vulnerable_employment[30, "COUNTRY"] <- "Brunei"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated

vulnerable_employment[42, "COUNTRY"] <- "Congo (Kinshasa)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated

vulnerable_employment[43, "COUNTRY"] <- "Congo (Brazzaville)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated

vulnerable_employment[53, "COUNTRY"] <- "Czechia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated

vulnerable_employment[66, "COUNTRY"] <- "Egypt"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated

vulnerable_employment[85, "COUNTRY"] <- "Gambia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated

vulnerable_employment[111, "COUNTRY"] <- "Iran"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated

vulnerable_employment[125, "COUNTRY"] <- "Korea, South"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated

vulnerable_employment[121, "COUNTRY"] <- "Kyrgyzstan"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated

vulnerable_employment[128, "COUNTRY"] <- "Laos"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated

vulnerable_employment[132, "COUNTRY"] <- "Saint Lucia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated

vulnerable_employment[159, "COUNTRY"] <- "Burma"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated

vulnerable_employment[201, "COUNTRY"] <- "Russia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated

vulnerable_employment[220, "COUNTRY"] <- "Slovakia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated

vulnerable_employment[226, "COUNTRY"] <- "Syria"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated

vulnerable_employment[250, "COUNTRY"] <- "US"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated

vulnerable_employment[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated

vulnerable_employment[253, "COUNTRY"] <- "Venezuela"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated

vulnerable_employment[255, "COUNTRY"] <- "Virgin Islands"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated

vulnerable_employment[261, "COUNTRY"] <- "Yemen"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated

vulnerable_employment[7, "COUNTRY"] <- "United Arab Emirates"
vulnerable_employment[80, "COUNTRY"] <- "United Kingdom"
vulnerable_employment[179, "COUNTRY"] <- "New Zealand"
vulnerable_employment[204, "COUNTRY"] <- "Saudi Arabia"
vulnerable_employment[262, "COUNTRY"] <- "South Africa"

vulnerable_employment <- subset(vulnerable_employment, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(vulnerable_employment)

## [1] "COUNTRY" "vul_emp"

myvarsvulnerable_employment <- c("vul_emp", "COUNTRY")

vulnerable_employment <- vulnerable_employment[myvarsvulnerable_employment]

Final_Data_Country <- merge(Final_Data_Country, vulnerable_employment, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# Next, I add GNI per capita from: https://data.worldbank.org/indicator/NY.GNP.PCAP.CD?view=chart. 
GNI <- read.csv("data/GNI.csv")

# Next, I correct the country names.
GNI[22, "COUNTRY"] <- "Bahamas"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated

GNI[30, "COUNTRY"] <- "Brunei"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated

GNI[42, "COUNTRY"] <- "Congo (Kinshasa)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated

GNI[43, "COUNTRY"] <- "Congo (Brazzaville)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated

GNI[53, "COUNTRY"] <- "Czechia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated

GNI[66, "COUNTRY"] <- "Egypt"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated

GNI[85, "COUNTRY"] <- "Gambia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated

GNI[111, "COUNTRY"] <- "Iran"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated

GNI[125, "COUNTRY"] <- "Korea, South"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated

GNI[121, "COUNTRY"] <- "Kyrgyzstan"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated

GNI[128, "COUNTRY"] <- "Laos"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated

GNI[132, "COUNTRY"] <- "Saint Lucia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated

GNI[159, "COUNTRY"] <- "Burma"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated

GNI[201, "COUNTRY"] <- "Russia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated

GNI[220, "COUNTRY"] <- "Slovakia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated

GNI[226, "COUNTRY"] <- "Syria"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated

GNI[250, "COUNTRY"] <- "US"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated

GNI[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated

GNI[253, "COUNTRY"] <- "Venezuela"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated

GNI[255, "COUNTRY"] <- "Virgin Islands"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated

GNI[261, "COUNTRY"] <- "Yemen"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated

GNI[7, "COUNTRY"] <- "United Arab Emirates"
GNI[80, "COUNTRY"] <- "United Kingdom"
GNI[179, "COUNTRY"] <- "New Zealand"
GNI[204, "COUNTRY"] <- "Saudi Arabia"
GNI[262, "COUNTRY"] <- "South Africa"

GNI <- subset(GNI, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(GNI)

## [1] "COUNTRY" "GNI"

myvarsGNI <- c("GNI", "COUNTRY")

GNI <- GNI[myvarsGNI]

Final_Data_Country <- merge(Final_Data_Country, GNI, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# Next, I control for current health expenditures (l.y.a. is 2017): https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD?view=chart. 
health_pc <- read.csv("data/health_pc.csv")

# Next, I correct the country names.
health_pc[22, "COUNTRY"] <- "Bahamas"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated

health_pc[30, "COUNTRY"] <- "Brunei"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated

health_pc[42, "COUNTRY"] <- "Congo (Kinshasa)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated

health_pc[43, "COUNTRY"] <- "Congo (Brazzaville)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated

health_pc[53, "COUNTRY"] <- "Czechia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated

health_pc[66, "COUNTRY"] <- "Egypt"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated

health_pc[85, "COUNTRY"] <- "Gambia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated

health_pc[111, "COUNTRY"] <- "Iran"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated

health_pc[125, "COUNTRY"] <- "Korea, South"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated

health_pc[121, "COUNTRY"] <- "Kyrgyzstan"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated

health_pc[128, "COUNTRY"] <- "Laos"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated

health_pc[132, "COUNTRY"] <- "Saint Lucia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated

health_pc[159, "COUNTRY"] <- "Burma"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated

health_pc[201, "COUNTRY"] <- "Russia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated

health_pc[220, "COUNTRY"] <- "Slovakia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated

health_pc[226, "COUNTRY"] <- "Syria"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated

health_pc[250, "COUNTRY"] <- "US"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated

health_pc[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated

health_pc[253, "COUNTRY"] <- "Venezuela"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated

health_pc[255, "COUNTRY"] <- "Virgin Islands"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated

health_pc[261, "COUNTRY"] <- "Yemen"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated

health_pc[7, "COUNTRY"] <- "United Arab Emirates"
health_pc[80, "COUNTRY"] <- "United Kingdom"
health_pc[179, "COUNTRY"] <- "New Zealand"
health_pc[204, "COUNTRY"] <- "Saudi Arabia"
health_pc[262, "COUNTRY"] <- "South Africa"

health_pc <- subset(health_pc, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(health_pc)

## [1] "COUNTRY"    "health_exp"

myvarshealth_pc <- c("health_exp", "COUNTRY")

health_pc <- health_pc[myvarshealth_pc]

Final_Data_Country <- merge(Final_Data_Country, health_pc, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

# I also add data on air pollution by nation from: https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3?view=chart. 
pollution <- read.csv("data/pollution.csv")

# Next, I correct the country names.
pollution[22, "COUNTRY"] <- "Bahamas"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Bahamas"): invalid factor level,
## NA generated

pollution[30, "COUNTRY"] <- "Brunei"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Brunei"): invalid factor level,
## NA generated

pollution[42, "COUNTRY"] <- "Congo (Kinshasa)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Kinshasa)"): invalid
## factor level, NA generated

pollution[43, "COUNTRY"] <- "Congo (Brazzaville)"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Congo (Brazzaville)"): invalid
## factor level, NA generated

pollution[53, "COUNTRY"] <- "Czechia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Czechia"): invalid factor level,
## NA generated

pollution[66, "COUNTRY"] <- "Egypt"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Egypt"): invalid factor level,
## NA generated

pollution[85, "COUNTRY"] <- "Gambia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Gambia"): invalid factor level,
## NA generated

pollution[111, "COUNTRY"] <- "Iran"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Iran"): invalid factor level, NA
## generated

pollution[125, "COUNTRY"] <- "Korea, South"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Korea, South"): invalid factor
## level, NA generated

pollution[121, "COUNTRY"] <- "Kyrgyzstan"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Kyrgyzstan"): invalid factor
## level, NA generated

pollution[128, "COUNTRY"] <- "Laos"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Laos"): invalid factor level, NA
## generated

pollution[132, "COUNTRY"] <- "Saint Lucia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Lucia"): invalid factor
## level, NA generated

pollution[159, "COUNTRY"] <- "Burma"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Burma"): invalid factor level,
## NA generated

pollution[201, "COUNTRY"] <- "Russia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Russia"): invalid factor level,
## NA generated

pollution[220, "COUNTRY"] <- "Slovakia"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Slovakia"): invalid factor
## level, NA generated

pollution[226, "COUNTRY"] <- "Syria"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Syria"): invalid factor level,
## NA generated

pollution[250, "COUNTRY"] <- "US"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "US"): invalid factor level, NA
## generated

pollution[252, "COUNTRY"] <- "Saint Vincent and the Grenadines"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Saint Vincent and the
## Grenadines"): invalid factor level, NA generated

pollution[253, "COUNTRY"] <- "Venezuela"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Venezuela"): invalid factor
## level, NA generated

pollution[255, "COUNTRY"] <- "Virgin Islands"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Virgin Islands"): invalid factor
## level, NA generated

pollution[261, "COUNTRY"] <- "Yemen"

## Warning in `[<-.factor`(`*tmp*`, iseq, value = "Yemen"): invalid factor level,
## NA generated

pollution[7, "COUNTRY"] <- "United Arab Emirates"
pollution[80, "COUNTRY"] <- "United Kingdom"
pollution[179, "COUNTRY"] <- "New Zealand"
pollution[204, "COUNTRY"] <- "Saudi Arabia"
pollution[262, "COUNTRY"] <- "South Africa"

pollution <- subset(pollution, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(pollution)

## [1] "COUNTRY"   "pollution"

myvarspollution <- c("pollution", "COUNTRY")

pollution <- pollution[myvarspollution]

Final_Data_Country <- merge(Final_Data_Country, pollution, by = c("COUNTRY"), all=TRUE)

# Here, I add democracy indicators from Freedom House: https://freedomhouse.org/countries/freedom-world/scores and the Economist: https://www.eiu.com/topic/democracy-index?&zid=democracyindex2019&utm_source=blog&utm_medium=blog&utm_name=democracyindex2019&utm_term=democracyindex2019&utm_content=top_link. 

democracy <- read.csv("data/democracy.csv")

democracy <- subset(democracy, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(democracy)

## [1] "COUNTRY"       "EUI_democracy" "freedom_house"

myvarsdemocracy <- c("EUI_democracy", "freedom_house", "COUNTRY")

democracy <- democracy[myvarsdemocracy]

Final_Data_Country <- merge(Final_Data_Country, democracy, by = c("COUNTRY"), all=TRUE)

# Here, I add data on the number of cellular subscriptions by nation from: https://data.worldbank.org/indicator/IT.CEL.SETS.P2?start=1960.

cellular <- read.csv("data/cellular.csv")

cellular <- subset(cellular, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(cellular)

## [1] "COUNTRY"      "cellular_sub"

myvarscellular <- c("cellular_sub", "COUNTRY")

cellular <- cellular[myvarscellular]

Final_Data_Country <- merge(Final_Data_Country, cellular, by = c("COUNTRY"), all=TRUE)

# Now, I add military data from: https://correlatesofwar.org/data-sets/national-material-capabilities. Note the l.y.a. is 2012.

military <- read.csv("data/military.csv")

military <- subset(military, COUNTRY %in% Final_Data_Country$COUNTRY)

# I only keep the columns I care about.
colnames(military)

## [1] "COUNTRY" "milex"   "milper"  "irst"    "pec"     "cinc"

myvarsmilitary <- c("milex", "milper", "irst", "pec", "cinc", "COUNTRY")

military <- military[myvarsmilitary]

Final_Data_Country <- merge(Final_Data_Country, military, by = c("COUNTRY"), all=TRUE)

Adding data on prior deaths from diseases.

# I now add data on diseases (aggregated from 2015-2018) from the WHO ICD10: https://www.who.int/classifications/icd/icdonlineversions/en/. 
diseases <- read.csv("data/ICD_Deaths_Final.csv")

diseases <- subset(diseases, COUNTRY %in% Final_Data_Country$COUNTRY)

Final_Data_Country <- merge(Final_Data_Country, diseases, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

Performing final edits and saving the dataframe (plus filtering the sample).

# Labelling our variables.
label(Final_Data_Country$C1_School.closing) <- "Record Closings of Schools and Universities (0 - No Measures; 1 - Recommended Closing; 2 - Required Closing (some levels); 3 - Required Closing (all levels))"
label(Final_Data_Country$C1_Flag) <- "Targeted vs. General School Closings (0 - Targeted; 1 - General)"
label(Final_Data_Country$C2_Workplace.closing) <- "Record Closings of Workplaces (0 - No Measures; 1 - Recommended Closing; 2 - Required Closing (some sectors); 3 - Required Closing (all but essential workers))"
label(Final_Data_Country$C2_Flag) <- "Targeted vs. General Workplace Closings (0 - Targeted; 1 - General)"
label(Final_Data_Country$C3_Cancel.public.events) <- "Record Closings of Public Events (0 - No Measures; 1 - Recommended Cancelling; 2 - Required Cancelling)"
label(Final_Data_Country$C3_Flag) <- "Targeted vs. General Public Event Closings (0 - Targeted; 1 - General)"
label(Final_Data_Country$C4_Restrictions.on.gatherings) <- "Restrictions on Public Gatherings (0 - No Measures; 1 - Restrictions on Gatherings of 1000 + people; 2 - Restrictions on Gatherings of 100-1000 people; 3 - Restrictions on Gatherings of 10-100 people; 4 - Restrictions on Gatherings of < 10 people)"
label(Final_Data_Country$C4_Flag) <- "Targeted vs. General Public Gathering Restrictions (0 - Targeted; 1 - General)"
label(Final_Data_Country$C5_Close.public.transport) <- "Record Closings of Public Transport (0 - No Measures; 1 - Recommended Closing (means of transport reduced); 2 - Required Closing (prohibit public transit operations))"
label(Final_Data_Country$C5_Flag) <- "Targeted vs. General Public Transport Closings (0 - Targeted; 1 - General)"
label(Final_Data_Country$C6_Stay.at.home.requirements) <- "Lockdown Measures (0 - No Measures; 1 - Recommendeded; 2 - Lax Lockdown (allows for exercise, shopping, and essential trips); 3 - Strict Lockdown)"
label(Final_Data_Country$C6_Flag) <- "Targeted vs. General Lockdown (0 - Targeted; 1 - General)"
label(Final_Data_Country$C7_Restrictions.on.internal.movement) <- "Record Restrictions on Internal Movement (0 - No Measures; 1 - Recommend Movement Restriction (reduce means of transport); 2 - Restrict Movement (prohibit internal movement))"
label(Final_Data_Country$C7_Flag) <- "Targeted vs. General Restrictions on Internal Movement (0 - Targeted; 1 - General)"
label(Final_Data_Country$C8_International.travel.controls) <- "Record Restrictions on International Travel (0 - No Measures; 1 - Screening; 2 - Quarantine on High-Risk Regions; 3 - Ban on High-Risk Regions)"
label(Final_Data_Country$E1_Income.support) <- "Income Support (0 - None; 1 - Govt. Funds < 50% of Lost Salary; 2 - Govt. Funds > 50% of Lost Salary)"
label(Final_Data_Country$E1_Flag) <- "Targeted vs. General Income Support"
label(Final_Data_Country$E2_Debt.contract.relief) <- "Debt Relief (0 - None; 1 - Narrow Relief (specific kinds of contracts); 2 - Broad Relief)"
label(Final_Data_Country$E3_Fiscal.measures) <- "Value of Fiscal Stimuli, Including Spending or Tax Cuts (in USD)"
label(Final_Data_Country$H1_Public.information.campaigns) <- "Record Presence of Public Information Campaigns (0 - No; 1 - Public Officials Urge Caution; 2 - Coordinated Public Information Campaign)"
label(Final_Data_Country$H1_Flag) <- "Targeted vs. General Public Information Campaigns (0 - Targeted; 1 - General)"
label(Final_Data_Country$H2_Testing.policy) <- "Who Can Get Tested (0 - No Testing Policy; 1 - Testing for those with a) Symptoms AND b) Meet Given Criteria (key workers; admitted to hospital; came into contact with known patient; returned from overseas); 2 - Testing of anyone showing symptoms; 3 - Open Public Testing ('drive-through testing')"
label(Final_Data_Country$H3_Contact.tracing) <- "Contact Tracing (0 - No Contact Tracing; 1 - Limited Contact Tracing; 2 - Comprehensive Contact Tracing)"
label(Final_Data_Country$H4_Emergency.investment.in.healthcare) <- "Value of New Short-Term Spending on Health (in USD)"
label(Final_Data_Country$H5_Investment.in.vaccines) <- "Value of Investment (in USD)"
label(Final_Data_Country$StringencyIndex) <- "Index for Government Stringency (calculated using C1-S8 + H1)"
label(Final_Data_Country$LegacyStringencyIndex) <- "Legacy Index for Government Stringency (converts new calculation to prior calculation for our 'original' Stringency Index)"
label(Final_Data_Country$Oxford_Cases) <- "Cumulative Sum of Confirmed Cases by Country (Oxford)"
label(Final_Data_Country$Oxford_Deaths) <- "Cumulative Sum of Deaths by Country (Oxford)"
label(Final_Data_Country$prop65) <- "Proportion of the Population Above Age 65"
label(Final_Data_Country$propurban) <- "Proportion of Population in Urban Areas"
label(Final_Data_Country$popdensity) <- "People per 100 Sq. Km. of Land Area"
label(Final_Data_Country$arrivals) <- "Number of Tourist Arrivals"
label(Final_Data_Country$departures) <- "Number of Tourist Departures"
label(Final_Data_Country$vul_emp) <- "Percentage of Population in Vulnerable Employment"
label(Final_Data_Country$GNI) <- "GNI per capita (current USD - Atlas Method) "
label(Final_Data_Country$health_exp) <- "Health Expenditure Per Capita"
label(Final_Data_Country$pollution) <- "PM2.5 Air Pollution (micrograms per cubic meter)"
label(Final_Data_Country$EUI_democracy) <- "Democracy scores from the Economist Intelligence Unit"
label(Final_Data_Country$freedom_house) <- "Democracy scores from Freedom House"
label(Final_Data_Country$milex) <- "Military expenditures (thousands of GBP)"
label(Final_Data_Country$milper) <- "Military personnel (thousands of soldiers)"
label(Final_Data_Country$irst) <- "Iron and steel production (thousands of ton)"
label(Final_Data_Country$pec) <- "Primary energy consumption (thousands of coal-ton equivalents)"
label(Final_Data_Country$cinc) <- "Composite Index of National Capability score"

# For now, we are restricting our analysis to OECD/EM markets. I do so below, using a loose definition of EMs from: https://www.msci.com/documents/10199/c0db0a48-01f2-4ba9-ad01-226fd5678111 and advanced economies from: https://www.imf.org/external/pubs/ft/weo/2020/01/weodata/groups.htm. Provinces of China (Taiwan, Macao, Hong Kong) and Italy (San Marino) are aggregated to the national level. Note that Lithuania, while included here for visualizations, does not have government responses data and is thus not run in the regressions.

Final_Data_Country <- subset(Final_Data_Country, (COUNTRY == "Argentina" | COUNTRY == "Australia" | COUNTRY == "Austria" | COUNTRY == "Belgium" | COUNTRY == "Brazil" | COUNTRY == "Canada" | COUNTRY == "Chile" | COUNTRY == "China" | COUNTRY == "Colombia" | COUNTRY == "Cyprus" | COUNTRY == "Czechia" |COUNTRY == "Denmark" | COUNTRY == "Egypt" | COUNTRY == "Estonia" | COUNTRY == "Finland" | COUNTRY == "France" | COUNTRY == "Germany" | COUNTRY == "Greece" | COUNTRY == "Hungary" | COUNTRY == "Iceland" | COUNTRY == "India" | COUNTRY == "Indonesia" | COUNTRY == "Ireland" | COUNTRY == "Israel" | COUNTRY == "Italy" | COUNTRY == "Japan" | COUNTRY == "Jordan" | COUNTRY == "Korea, South" | COUNTRY == "Kuwait" | COUNTRY == "Latvia" | COUNTRY == "Lithuania" | COUNTRY == "Luxembourg" | COUNTRY == "Malaysia" | COUNTRY == "Malta" | COUNTRY == "Mexico" | COUNTRY == "Morocco" | COUNTRY == "Netherlands" | COUNTRY == "New Zealand" |COUNTRY == "Norway" | COUNTRY == "Peru" | COUNTRY == "Philippines" | COUNTRY == "Poland" | COUNTRY == "Portugal" | COUNTRY == "Qatar" | COUNTRY == "Russia" | COUNTRY == "Saudi Arabia" | COUNTRY == "Singapore" | COUNTRY == "Slovakia" | COUNTRY == "Slovenia" |  COUNTRY == "South Africa" | COUNTRY == "Spain" | COUNTRY == "Sweden" | COUNTRY == "Switzerland" | COUNTRY == "Thailand" | COUNTRY == "Turkey" | COUNTRY == "United Arab Emirates" | COUNTRY == "United Kingdom" | COUNTRY == "US" | COUNTRY == "Vietnam"))

# Next, I label and clean the final dataset. This is excluded for brevity.

We now have a final panel dataset. I import the saved CSV, to ensure there are no discrepancies.

remove(Final_Data_Country)

#  Note that I change the column types so reader can properly parse the data. This dataset also includes lagged variables for the mortality rate, which are not generated in the code for brevity.

library(readr)
Final_Data_Country <- read_csv(("data/Final_Data_Country.csv"), 
  col_types = cols(`E2_Debt.contract.relief` = col_number(),
                   `Lagged_Debt_Relief_One_Week` = col_number(),
                   `Lagged_Debt_Relief_Two_Weeks` = col_number(),
                   `Lagged_Debt_Relief_Three_Weeks` = col_number(),
                   `E1_Income.support` = col_number(),
                   `Lagged_Income_Support_One_Week` = col_number(),
                   `Lagged_Income_Support_Two_Weeks` = col_number(),
                   `Lagged_Income_Support_Three_Weeks` = col_number()))

## Warning: Missing column names filled in: 'X1' [1]

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

Adding data on government indicators.

# I now add data on government indicators from: https://info.worldbank.org/governance/wgi/Home/Documents. 
govt <- read.csv("data/govt.csv")

govt <- subset(govt, COUNTRY %in% Final_Data_Country$COUNTRY)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

Final_Data_Country <- merge(Final_Data_Country, govt, by = c("COUNTRY"), all=TRUE)

Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

Next, I want to merge the spatial data. We do this after the final dataset has been filtered, to ensure a smaller dataset size.

# Now, we want to integrate our spatial datasets from CEPII. 

geo_cepii <- read_csv("data/spatial_data/geo_cepii.csv")

## Parsed with column specification:
## cols(
##   .default = col_character(),
##   cnum = col_double(),
##   area = col_double(),
##   dis_int = col_double(),
##   landlocked = col_double(),
##   lat = col_double(),
##   lon = col_double(),
##   cap = col_double(),
##   maincity = col_double(),
##   citynum = col_double()
## )

## See spec(...) for full column specifications.

# We only have country-level information for our COVID data. So, we want to collapse the geo_cepii data to the country level, without losing the local variation provided by our coordinates and distances for city indicators. However, in the case where we have multiple cities per country, we must make a decision on how to collapse. I choose to only keep the capital cities, and collapse by countries in this way.

geo_cepii_final <- geo_cepii %>%
  dplyr:: filter(geo_cepii$cap == 1)

geo_cepii_final$COUNTRY <- geo_cepii_final$country
geo_cepii_final$City <- geo_cepii_final$city_en
geo_cepii_final$City_Latitude <- geo_cepii_final$lat
geo_cepii_final$City_Longitude <- geo_cepii_final$lon
geo_cepii_final$Capital <- geo_cepii_final$cap

# Below, I create a new dataframe for merging the geo_cepii data with the distance_cepii data.
geo_cepii_merge <- geo_cepii_final[c(2,35)]

# Now, I read in the distance_cepii data. 
distance_cepii <- read_csv("data/spatial_data/distance_cepii.csv")

## Parsed with column specification:
## cols(
##   iso_o = col_character(),
##   iso_d = col_character(),
##   contig = col_double(),
##   comlang_off = col_double(),
##   comlang_ethno = col_double(),
##   colony = col_double(),
##   comcol = col_double(),
##   curcol = col_double(),
##   col45 = col_double(),
##   smctry = col_double(),
##   dist = col_double(),
##   distcap = col_double(),
##   distw = col_double(),
##   distwces = col_double()
## )

# We want to merge by both ISO values, and match the ISOs to country names. I do this below.
geo_cepii_merge$iso_d <- geo_cepii_merge$iso3

merge_1 <- merge(geo_cepii_merge, distance_cepii, by = c("iso_d"), all=TRUE)

geo_cepii_merge$iso_o <- geo_cepii_merge$iso_d

merge_2 <- merge(geo_cepii_merge, merge_1, by = c("iso_o"), all=TRUE)

merge_2$COUNTRY <- merge_2$COUNTRY.x
merge_2$COUNTRY_Dyad <- merge_2$COUNTRY.y

colnames(merge_2)

##  [1] "iso_o"         "iso3.x"        "COUNTRY.x"     "iso_d.x"      
##  [5] "iso_d.y"       "iso3.y"        "COUNTRY.y"     "contig"       
##  [9] "comlang_off"   "comlang_ethno" "colony"        "comcol"       
## [13] "curcol"        "col45"         "smctry"        "dist"         
## [17] "distcap"       "distw"         "distwces"      "COUNTRY"      
## [21] "COUNTRY_Dyad"

Cepii_Data_Final <- merge(geo_cepii_final, merge_2, by = c("COUNTRY"), all=TRUE)

# I keep only the relevant variables.

myvarscepiifinal <- c("COUNTRY", "COUNTRY_Dyad", "dist", "distcap", "distw", "distwces", "area", "dis_int", "landlocked", "continent", "City", "City_Latitude", "City_Longitude", "Capital", "maincity", "langoff_1", "langoff_2", "langoff_3", "lang20_1", "lang20_2", "lang20_3", "lang20_4", "lang9_1", "lang9_2", "lang9_3", "lang9_4", "colonizer1", "colonizer2", "colonizer3", "colonizer4", "short_colonizer1", "short_colonizer2", "short_colonizer3", "contig", "comlang_off", "comlang_ethno", "colony", "comcol", "curcol", "col45", "smctry")

Cepii_Data_Final <- Cepii_Data_Final[myvarscepiifinal]

label(Cepii_Data_Final$landlocked) <- "Dummy Variable for Landlocked (0 = No; 1 = Yes)"
label(Cepii_Data_Final$Capital) <- "Dummy Variable for City being Capital City (0 = No; 1 = Yes)"
label(Cepii_Data_Final$maincity) <- "Dummy Variable for City being Main City (0 = No; 1 = Yes)"

# Next, we want to merge our spatial data from CEPII to our COVID dataset. I don't do this here due to the size of the resulting file, and the runtime. If you wish to view the merged file, see "Spatial_Merged.csv" in the spatial_data folder.

INTERACTIVE VISUALIZATIONS

# I start by making a marker map which captures the number of confirmed cases on the first date of recorded data. To isolate these values, we must filter the dataframe to include observations for this date.

X1.23.20 <- Temporary_Data_Country %>%
  dplyr:: filter(Date == "2020-01-23")

# First, I want to visualize the "relative intensity" of cases across the world (using a consistent scale). To do this, I cut my variable into six quantiles, based on the values of confirmed cases from the LAST available date (3-31-20).
X1.23.20$magrangeconfirmed = cut(X1.23.20$Total_Cases_Country,
                          breaks = c(0, 1000, 10000, 100000, 250000, 500000, 1025000),
                          right=FALSE, labels = c("Very Low [0-1000)", "Low     
                          [1000-10000)", "Moderate [10000-100000)", "High [100000-250000)",
                          "Very High [250000-500000)", "Extreme [500000-1025000]"))

# I also want to jitter the latitude and longitude of my data points, in case our observations are clustered. We want to introduce a jitter which is large enough to differentiate markers, but not so large as to affect our spatial accuracy. I use a factor of 1.
X1.23.20$Lat <- jitter(X1.23.20$Latitude, factor = 1)

## Warning: Unknown or uninitialised column: `Latitude`.

X1.23.20$Long <- jitter(X1.23.20$Longitude, factor = 1)

## Warning: Unknown or uninitialised column: `Longitude`.

# To ease the user's interpretation of our data, we want to create an interactive label which provides information on each data point when the user hovers over the markers.
X1.23.20$labelconfirmed <- paste ("<Country: >", X1.23.20$COUNTRY, "<p>",
                         "<Confirmed Cases: >", X1.23.20$Total_Cases_Country, "<p>")

# Finally, we can construct our initial marker map. First, I set different colors for each quantile.
pal = colorFactor(palette = c("#CCCCCC", "#999999", "#99FF33", "#FFCC33", "#FF6600", "#990000"), domain=X1.23.20$magrangeconfirmed)

# Now, I pass the data through leaflet and generate the map.
Plot_1 <- leaflet(data=X1.23.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(data=X1.23.20, lng = ~longitude, lat = ~latitude,
                 # Here, I add the color pallete to shade the markers by intensity.
                 color = ~ pal(magrangeconfirmed),
                 weight = 5,
                 radius = 10,
                 label = lapply(X1.23.20$labelconfirmed, HTML),
                 clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright", pal = pal, values = ~magrangeconfirmed,
    title = "Total Confirmed Cases - COVID19 (January 23rd, 2020)",
    opacity = 1)

## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored

Plot_1

# Now, I repeat the process for deaths. Note that in both of these maps, we see low values (which makes sense, given this was the start of the pandemic).
X1.23.20$magrangedeceased = cut(X1.23.20$Total_Deceased_Country,
                          breaks = c(0, 100, 500, 1000, 5000, 25000, 60000), right=FALSE,
                          labels = c("Very Low [0-100)", "Low [100-500)", "Moderate [500-1000)", "High [1000-5000)", "Very High   
                                     [5000-25000)", "Extreme [25000-60000]"))
X1.23.20$Lat <- jitter(X1.23.20$Latitude, factor = 1)

## Warning: Unknown or uninitialised column: `Latitude`.

X1.23.20$Long <- jitter(X1.23.20$Longitude, factor = 1)

## Warning: Unknown or uninitialised column: `Longitude`.

X1.23.20$labeldeceased <- paste ("<Country: >", X1.23.20$COUNTRY, "<p>",
                         "<Deceased: >", X1.23.20$Total_Deceased_Country, "<p>")
pal = colorFactor(palette = c("#CCCCCC", "#999999", "#99FF33", "#FFCC33", "#FF6600", "#990000"), domain=X1.23.20$magrangedeceased)
Plot_2 <- leaflet(data=X1.23.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(data=X1.23.20, lng = ~longitude, lat = ~latitude,
                 color = ~ pal(magrangedeceased),
                 weight = 5,
                 radius = 10,
                 label = lapply(X1.23.20$labeldeceased, HTML),
                 clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright", pal = pal, values = ~magrangedeceased,
    title = "Total Deaths - COVID19 (January 23rd, 2020)",
    opacity = 1)

## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored

Plot_2

# Now, I repeat the same procedure for the last date in our dataset.
X4.28.20 <- Temporary_Data_Country %>%
  dplyr:: filter(Date == "2020-04-28")

X4.28.20$magrangeconfirmed2 = cut(X4.28.20$Total_Cases_Country, 
                          breaks = c(0, 1000, 10000, 100000, 250000, 500000, 1025000),
                          right=FALSE, labels = c("Very Low [0-1000)", "Low     
                          [1000-10000)", "Moderate [10000-100000)", "High [100000-250000)",
                          "Very High [250000-500000)", "Extreme [500000-1025000]"))
X4.28.20$Lat <- jitter(X4.28.20$Latitude, factor = 1)

## Warning: Unknown or uninitialised column: `Latitude`.

X4.28.20$Long <- jitter(X4.28.20$Longitude, factor = 1)

## Warning: Unknown or uninitialised column: `Longitude`.

X4.28.20$labelconfirmed <- paste ("<Country: >", X4.28.20$COUNTRY, "<p>",
                         "<Confirmed Cases: >", X4.28.20$Total_Cases_Country, "<p>")

# Finally, we can construct our initial marker map. First, I set different colors for each quantile.
pal = colorFactor(palette = c("#CCCCCC", "#999999", "#99FF33", "#FFCC33", "#FF6600", "#990000"), domain=X4.28.20$magrangeconfirmed2)
# Now, I pass the data through leaflet and generate the map.
Plot_3 <- leaflet(data=X4.28.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(data=X4.28.20, lng = ~longitude, lat = ~latitude,
                 # Here, I add the color pallete to shade the markers by intensity.
                 color = ~ pal(magrangeconfirmed2),
                 weight = 5,
                 radius = 10,
                 label = lapply(X4.28.20$labelconfirmed, HTML),
                 clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright", pal = pal, values = ~magrangeconfirmed2,
    title = "Total Confirmed Cases - COVID19 (April 28th, 2020)",
    opacity = 1)

## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored

Plot_3

X4.28.20$magrangedeceased2 = cut(X4.28.20$Total_Deceased_Country,
                          breaks = c(0, 100, 500, 1000, 5000, 25000, 60000), right=FALSE,
                          labels = c("Very Low [0-100)", "Low [100-500)", "Moderate [500-1000)", "High [1000-5000)", "Very High   
                                     [5000-25000)", "Extreme [25000-60000]"))
X4.28.20$Lat <- jitter(X4.28.20$Latitude, factor = 1)

## Warning: Unknown or uninitialised column: `Latitude`.

X4.28.20$Long <- jitter(X4.28.20$Longitude, factor = 1)

## Warning: Unknown or uninitialised column: `Longitude`.

X4.28.20$labeldeceased <- paste ("<Country: >", X4.28.20$COUNTRY, "<p>",
                         "<Deceased: >", X4.28.20$Total_Deceased_Country, "<p>")
pal = colorFactor(palette = c("#CCCCCC", "#999999", "#99FF33", "#FFCC33", "#FF6600", "#990000"), domain=X4.28.20$magrangedeceased2)
Plot_4 <- leaflet(data=X4.28.20) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(data=X4.28.20, lng = ~longitude, lat = ~latitude,
                 color = ~ pal(magrangedeceased2),
                 weight = 5,
                 radius = 10,
                 label = lapply(X4.28.20$labeldeceased, HTML),
                 clusterOptions = markerClusterOptions()) %>%
addLegend("bottomright", pal = pal, values = ~magrangedeceased2,
    title = "Total Deaths - COVID19 (April 28th, 2020)",
    opacity = 1)

## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored

Plot_4

# Now we create bubble maps for confirmed and deceased. Credit for some of these visualizations goes to: Yanchang Zhao, COVID-19 Data Analysis with R – Worldwide. RDataMining.com, 2020. URL: http://www.rdatamining.com/docs/Coronavirus-data-analysis-world.pdf. 
Plot_5 <- leaflet(data = X4.28.20) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>%
  addCircleMarkers(X4.28.20$longitude, X4.28.20$latitude,
  radius=2+log2(X4.28.20$Total_Cases_Country), stroke=F,
  color='red', fillOpacity=0.3, label = lapply(X4.28.20$labelconfirmed, HTML))

## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored

Plot_5

Plot_6 <- leaflet(data = X4.28.20) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>%
  addCircleMarkers(X4.28.20$longitude, X4.28.20$latitude,
  radius=2+log2(X4.28.20$Total_Deceased_Country), stroke=F,
  color='black', fillOpacity=0.3, label = lapply(X4.28.20$labeldeceased, HTML))

## Warning in validateCoords(lng, lat, funcName): Data contains 1 rows with either
## missing or invalid lat/lon values and will be ignored

Plot_6

Choropleth Maps

# First, filter the data and create my labels.
# Choropleth_Country <- Temporary_Data_Country %>%
#   dplyr:: filter(Date == "2020-04-28") 
 
# Choropleth_Country$choroplethlabelsconfirmed <- paste ("<Country: >", Choropleth_Country$COUNTRY, "<p>", "<Confirmed Total: >", Choropleth_Country$Total_Cases_Country, "<p>")

# Then, I read in my shape file. 
# Shape_File <- st_read("/data/UIA_World_Countries_Boundaries/UIA_World_Countries_Boundaries.shp")
# str(Shape_File)
 
# In order to properly use the shape file, I need to make a few edits to our country names in the data.
 
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Czechia", "Czech Republic", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Kyrgyzstan", "Krygz Republic", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Korea, South", "South Korea", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Russia", "Russian Federation", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Holy See", "Vatican City", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "US", "United States", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Cote d'Ivoire", "Côte d'Ivoire", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "French Guiana", "French Guiana", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Congo (Kinshasa)", "Congo DRC", Choropleth_Country$COUNTRY)
# Choropleth_Country$COUNTRY <- ifelse(Choropleth_Country$COUNTRY == "Congo (Brazzaville)", "Congo", Choropleth_Country$COUNTRY)

# Shape_File <- subset(Shape_File, is.element(Shape_File$COUNTRY, Choropleth_Country$COUNTRY))

# Choropleth_Country <- Choropleth_Country[order(match(Choropleth_Country$COUNTRY, Shape_File$COUNTRY)),]
 
# I also create a bin, separating the total confirmed cases into relevant quantiles. I then set colors for the bin values.
# binsconfirmed <- c(0, 5000, 25000, 50000, 100000, 250000, 1250000)
# palconfirmed <- colorBin("OrRd", domain = Choropleth_Country$Total_Cases_Country, bins = binsconfirmed)
# Plot_7 <- leaflet(data=Choropleth_Country) %>%
# addProviderTiles(providers$CartoDB.Positron) %>%
# addPolygons(data=Shape_File,
#   smoothFactor = 1,
#   weight = 2,
#   opacity = 1,
#   color = "black",
#   dashArray = "",
#   fillOpacity = 0.7,
#   fillColor = palconfirmed(Choropleth_Country$Total_Cases_Country),
#   highlight = highlightOptions(
#     weight = 5,
#     color = "#666",
#     dashArray = "",
#     fillOpacity = 0.7,
#     bringToFront = TRUE),
#   label = lapply(Choropleth_Country$choroplethlabelsconfirmed, HTML)) %>% 
# addLegend("bottomleft", pal = palconfirmed, values = ~binsconfirmed,
#     title = "Total Confirmed Cases - COVID19 (April 28th, 2020)",
#     opacity = 1)
# Plot_7

# Please note: this choropleth only provides a static version of current cases (just like the Bloomberg graph). I am working on creating a web app through R Shiny which will allow the user to select a range of dates to update the choropleth.

# I now repeat this process for deaths.
 
# Choropleth_Country$choroplethlabelsdeceased <- paste ("<Country: >", Choropleth_Country$COUNTRY, "<p>", "<Deceased Total: >", Choropleth_Country$Total_Deceased_Country, "<p>")
 
# binsdeceased <- c(0, 500, 1000, 5000, 25000, 60000)
# paldeceased <- colorBin("OrRd", domain = Choropleth_Country$Total_Deceased_Country, bins = binsdeceased)
# Plot_8 <- leaflet(data=Choropleth_Country) %>%
# addProviderTiles(providers$CartoDB.Positron) %>%
# addPolygons(data=Shape_File,
#   smoothFactor = 1,
#   weight = 2,
#   opacity = 1,
#   color = "black",
#   dashArray = "",
#   fillOpacity = 0.7,
#   fillColor = paldeceased(Choropleth_Country$Total_Deceased_Country),
#   highlight = highlightOptions(
#     weight = 5,
#     color = "#666",
#     dashArray = "",
#     fillOpacity = 0.7,
#     bringToFront = TRUE),
#   label = lapply(Choropleth_Country$choroplethlabelsdeceased, HTML)) %>% 
# addLegend("bottomleft", pal = paldeceased, values = ~binsdeceased,
#     title = "Total Deaths - COVID19 (April 28th, 2020)",
#     opacity = 1)
# Plot_8

TIME SERIES VISUALIZATIONS

# Here, I recreate a version of the first Bloomberg graph from below: https://www.bloomberg.com/graphics/2020-coronavirus-cases-world-map/. To isolate a nation of interest, simply click on it in the legend at right. You can isolate multiple nations at once. I use the final data here, which is only our developed/emerging markets of interest.
Final_Data_Country <- Final_Data_Country[order(Final_Data_Country$COUNTRY, Final_Data_Country$Date),]

Days_Since_100_Confirmed <- Final_Data_Country %>%
  dplyr::filter(Total_Cases_Country >= 100)

Days_Since_100_Confirmed <- Days_Since_100_Confirmed %>%
  group_by(COUNTRY) %>%
  mutate(Days = row_number(COUNTRY))

Plot_9 <- plot_ly(Days_Since_100_Confirmed, x=~Days, y=~Total_Cases_Country) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Days Since 100 Cases - COVID19", yaxis = list(type = "log"))
Plot_9

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
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# Now, I repeat the process for deaths.
Days_Since_100_Deceased <- Final_Data_Country %>%
  dplyr:: filter(Total_Deceased_Country >= 100)

Days_Since_100_Deceased <- Days_Since_100_Deceased %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(Days = row_number(COUNTRY))

Plot_10 <- plot_ly(Days_Since_100_Deceased, x=~Days, y=~Total_Deceased_Country) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Days Since 100 Deaths - COVID19")
Plot_10

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Days_Since_100_Deceased_Filtered <- Days_Since_100_Deceased %>%
  dplyr::filter(COUNTRY != "Malaysia")

# I also make a facet graph for days since 100 deaths, for countries in our sample.
Plot_10A <- ggplot(data = Days_Since_100_Deceased, aes(Days, scale(Total_Deceased_Country))) + geom_line() + facet_wrap(~COUNTRY, scales = "free", ncol = 9) + theme_bw() + theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.title.x=element_blank(),
        axis.ticks.x=element_blank(),
        strip.background = element_rect(color="white", fill="white"))
Plot_10A

## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

Plot_10B <- ggplot(data = Days_Since_100_Deceased_Filtered, aes(x = Days, y = scale(Total_Deceased_Country), group = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), axis.text.y=element_blank(), axis.title.y=element_text(size=9), axis.title.x=element_text(size=9)) + ylab("Deaths") + xlab("Days")
Plot_10B

Days_Since_100A <- Days_Since_100_Deceased_Filtered %>%
  dplyr::filter(COUNTRY != "China") %>%
  dplyr::filter(COUNTRY != "US") %>%
  dplyr::filter(COUNTRY != "Italy")

Days_Since_100B <- Days_Since_100_Deceased_Filtered %>%
  dplyr::filter(COUNTRY == "China" | COUNTRY == "US" | COUNTRY == "Italy")

Plot_10C <- ggplot(data = Days_Since_100_Deceased_Filtered, aes(x = Days, y = scale(Total_Deceased_Country), color = COUNTRY)) + geom_line() + theme_bw() + theme( axis.title.y=element_text(size=9), axis.title.x=element_text(size=9)) + gghighlight(COUNTRY == "US" | COUNTRY == "Italy" | COUNTRY == "China", use_direct_label = FALSE) + ylab("Deaths") + xlab("Days")

## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...

Plot_10C

# Here, I recreate a version of the third Bloomberg graph, plotting new global cases per day: https://www.bloomberg.com/graphics/2020-coronavirus-cases-world-map/.
New_Global_Cases <- Temporary_Data_Country %>%
  group_by(Date) %>%
  summarise(New_Cases = sum(New_Confirmed_Country))

New_Confirmed <- ggplot(data = New_Global_Cases, aes(x = Date, y = New_Cases)) +
      geom_bar(stat = "identity", fill = "dodgerblue") +
      labs(title = "New Confirmed Cases - COVID19",
           subtitle = "January 23rd, 2020 - April 28th, 2020",
           x = "Date", y = "New Confirmed Cases")
Plot_11 <- ggplotly(New_Confirmed)

## Warning: Removed 1 rows containing missing values (position_stack).

Plot_11

# Now, I do the same thing for deaths.
New_Global_Deaths <- Temporary_Data_Country %>%
  group_by(Date) %>%
  summarise(New_Deaths = sum(New_Total_Deceased_Country))

New_Deaths <- ggplot(data = New_Global_Deaths, aes(x = Date, y = New_Deaths)) +
      geom_bar(stat = "identity", fill = "sandybrown") +
      labs(title = "New Deaths - COVID19",
           subtitle = "January 23rd, 2020 - April 28th, 2020",
           x = "Date", y = "New Deaths")
Plot_12 <- ggplotly(New_Deaths)

## Warning: Removed 1 rows containing missing values (position_stack).

Plot_12

# Next, I construct a graph illustrating the seven-day rolling average of new deaths, new cases, and the mortality rate per capita. This is a version of the fourth Bloomberg plot from the above link. You will need to slide to the right (to March 2020) to see variation in these plots (or simply check the output folder). I use the Temporary Data, rather than the filtered Final Data, so that users may explore the entire set of nations available.

Temporary_Data_Country <- Temporary_Data_Country[order(Temporary_Data_Country$COUNTRY, Temporary_Data_Country$Date),]

Plot_13 <- plot_ly(Temporary_Data_Country, x=~Date, y=~rolling_average_confirmed) %>% add_lines(linetype = ~COUNTRY) %>% layout(title="Rolling Average of Confirmed Cases")
Plot_13

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## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_14 <- plot_ly(Temporary_Data_Country, x=~Date, y=~rolling_average_deceased) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Rolling Average of Deaths")
Plot_14

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_15 <- plot_ly(Temporary_Data_Country, x=~Date, y=~total_rolling_average_mortality) %>% add_lines(linetype = ~COUNTRY) %>% layout(title="Total Rolling Average of Mortality Rate per Capita")
Plot_15

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_16 <- plot_ly(Temporary_Data_Country, x=~Date, y=~new_rolling_average_mortality) %>% add_lines(linetype = ~COUNTRY) %>% layout(title="New Rolling Average of Mortality Rate per Capita")
Plot_16

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
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# Here, I plot the global confirmed cases over our time period.
exponential <- function(x) exp(x)
exponential_function <- ggplot(data = data.frame(x=c(0,10)), mapping=aes(x=x)) + stat_function(fun = exponential)
exponential_function

Confirmed <- ggplot(data = World_Data, aes(x = Date, y = Global_Confirmed_Cases)) +
      geom_bar(stat = "identity", fill = "red") +
      labs(title = "Global Confirmed Cases - COVID19",
           subtitle = "January 22nd, 2020 - April 28th, 2020",
           x = "Date", y = "Confirmed Cases")
Plot_17 <- ggplotly(Confirmed)
Plot_17

# Now, I plot the global deaths over our time period.
Deaths <- ggplot(data = World_Data, aes(x = Date, y = Global_Deceased)) +
      geom_bar(stat = "identity", fill = "darkred") +
      labs(title = "Global Deaths - COVID19",
           subtitle = "January 22nd, 2020 - April 16th, 2020",
           x = "Date", y = "Deaths")
Plot_18 <- ggplotly(Deaths)
Plot_18

# Now, I plot the mortality rate per capita over our time period, for our total and new mortality rates.
Total_Global_Mortality <- Temporary_Data_Country %>%
  group_by(Date) %>%
  summarise(Total_Mortality = sum(Total_Mortality_Rate_Per_Capita))

Total_Mortality <- ggplot(data = Total_Global_Mortality, aes(x = Date, y = Total_Mortality)) +
      geom_bar(stat = "identity", fill = "darkblue") +
      labs(title = "Total Global Mortality Rate - COVID19",
           subtitle = "January 22nd, 2020 - April 28th, 2020",
           x = "Date", y = "Total Mortality Rate Per Capita")
Plot_19 <- ggplotly(Total_Mortality)

## Warning: Removed 1 rows containing missing values (position_stack).

Plot_19

New_Global_Mortality <- Temporary_Data_Country %>%
  group_by(Date) %>%
  summarise(New_Mortality = sum(New_Mortality_Rate_Per_Capita))

options(scipen = 999)

Generating Figure 2.2.

New_Mortality <- ggplot(data = New_Global_Mortality, aes(x = Date, y = (New_Mortality * 100))) +
      geom_bar(stat = "identity", fill = "darkred") +
      labs(subtitle = "January 22nd, 2020 - April 28th, 2020", y = "New Mortality Rate Per Capita (%)") + theme_bw() + theme(axis.title.x=element_blank())
New_Mortality

## Warning: Removed 1 rows containing missing values (position_stack).

Plot_20 <- ggplotly(New_Mortality)

## Warning: Removed 1 rows containing missing values (position_stack).

Plot_20

# Now, I plot the change in the global death rate across the time period.
Death_Rate <- ggplot(World_Data, aes(x=Date)) +
geom_line(aes(y=Death_Rate, colour='Daily')) +
xlab('') + ylab('Death Rate (%)') + labs(title='Change in Death Rate (%)') +
theme(legend.position='bottom', legend.title=element_blank(),
legend.text=element_text(size=8),
legend.key.size=unit(0.5, 'cm'),
axis.text.x=element_text(angle=45, hjust=1))
Plot_21 <- ggplotly(Death_Rate)
Plot_21

# This plot compares the number of deaths to the number of confirmed cases across the time period.
Plots <- Temporary_Data_Country %>%
  group_by(Date) %>%
  summarise(Confirmed_Total = sum(Total_Cases_Country), Deceased_Total = sum(Total_Deceased_Country))

plot1 <- ggplot(data = Plots, aes(x = Date, y = Confirmed_Total)) +
      geom_line(color = "royalblue1")
plot2 <- ggplot(data = Plots, aes(x = Date, y = Deceased_Total)) +
      geom_line(color = "orangered2")

overlay2 <- ggplot(data = Plots, aes(x = Date)) +
      geom_line(aes(y = Confirmed_Total), color = "royalblue1") + geom_line(aes(y = Deceased_Total), color = "orangered2") + scale_y_continuous(name = "Total Confirmed Cases", sec.axis = sec_axis(trans = ~.*1, name = "Total Deaths"))
Plot_22 <- ggplotly(overlay2)
Plot_22

# Now, I extend this analysis to generate the number of confirmed cases and deaths by country.
Plot_23 <- plot_ly(Temporary_Data_Country, x=~Date, y=~Total_Cases_Country) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Confirmed Cases Across Countries")
Plot_23

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## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_24 <- plot_ly(Temporary_Data_Country, x=~Date, y=~Total_Deceased_Country) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Deaths Across Countries")
Plot_24

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_25 <- plot_ly(Temporary_Data_Country, x=~Date, y=~Total_Mortality_Rate_Per_Capita) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Total Mortality Rate Across Countries")
Plot_25

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_26 <- plot_ly(Temporary_Data_Country, x=~Date, y=~New_Mortality_Rate_Per_Capita) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="New Mortality Rate Across Countries")
Plot_26

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
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Weekly Growth Rate Visualizations

# Here, I filter by first deaths per country.
First_Death <- Final_Data_Country %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::filter(Total_Deceased_Country > 0)

# Now, I calculate the week-by-week growth rate in the rolling average of the mortality rate. This is equivalent to the log[total_total_rolling_average_mortality_(t) – total_total_rolling_average_mortality_(t-7)]. First, I create a variable for weeks by country. This tells us in what week of the year each nation had their first death.
First_Death$week_num = lubridate::week(ymd(First_Death$Date))

# Next, I generate week-over-week mortality growth rates. Should be (current week - previous week)/previous week. I use two distinct formulas for this calculation.

First_Death <- First_Death %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(total_mortality_growth = log((total_rolling_average_mortality/Lag(total_rolling_average_mortality,7))))

First_Death <- First_Death %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(total_mortality_growth_b <- (total_rolling_average_mortality-Lag(total_rolling_average_mortality,7))/Lag(total_rolling_average_mortality,7))

First_Death$total_mortality_growth_b  <- log(First_Death$`... <- NULL`,10)

First_Death <- First_Death %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(new_mortality_growth <- log((new_rolling_average_mortality/Lag(new_rolling_average_mortality,7)))) 

First_Death$new_mortality_growth  <- First_Death$`... <- NULL`

First_Death <- First_Death %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(new_mortality_growth_b <- (new_rolling_average_mortality-Lag(new_rolling_average_mortality,7))/Lag(new_rolling_average_mortality,7))

First_Death$new_mortality_growth_b  <- log(First_Death$`... <- NULL`,10)

## Warning: NaNs produced

# Please see the First Death dataset in the data folder.

# The following code allows us to visualize the rolling average of the mortality rate by country.
Plot_27A <- plot_ly(First_Death, x=~Date, y=~total_rolling_average_mortality) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Total Rolling Average of Mortality Rate Over Time - COVID19")
Plot_27A

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## Warning: The following are not valid linetype codes:
## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_27B <- plot_ly(First_Death, x=~Date, y=~new_rolling_average_mortality) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="New Rolling Average of Mortality Rate Over Time - COVID19")
Plot_27B

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
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## 'NA'
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## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

# Here, I implement a log axis.
Plot_28 <- Plot_27A %>% layout(yaxis = list(type = "log"))
Plot_28

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

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## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

First_Death_Filtered <- First_Death %>%
  dplyr::filter(COUNTRY != "Singapore") %>%
  dplyr::filter(COUNTRY != "Vietnam")

Plot_28A <- ggplot(data = First_Death_Filtered, aes(x = Date, y = log(total_rolling_average_mortality), group = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), axis.text.y=element_blank(), axis.title.y=element_text(size=9), axis.title.x=element_blank()) + ylab("Rolling Average Cumulative Mortality")
Plot_28A

# Here, I add a facet wrap for each nation in our sample.
Plot_28B <- ggplot(data = First_Death_Filtered, aes(Date, log(total_rolling_average_mortality))) + geom_line(size=.7) + facet_wrap(~COUNTRY, switch="x", ncol = 7) + theme_bw() + theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.x = element_blank(),
        strip.background = element_rect(color="white", fill="white"))

## Warning: 'switch' is deprecated.
## Use 'strip.position' instead.
## See help("Deprecated")

Plot_28B

Plot_28C <- ggplot(data = First_Death_Filtered, aes(x = Date, y = new_rolling_average_mortality, group = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), axis.text.y=element_blank(), axis.title.y=element_text(size=9), axis.title.x=element_blank()) + ylab("Rolling Average New Mortality")
Plot_28C

Generating Figures 1.1, 1.2 and 1.3.

library(gghighlight)

Plot_28D <- ggplot(data = First_Death_Filtered, aes(x = Date, y = new_rolling_average_mortality, color = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), legend.position = "none", axis.title.y=element_text(size=9), axis.title.x=element_text(size=9)) + ylab("Rolling Average New Mortality") + gghighlight(COUNTRY == "Italy" | COUNTRY == "China" | COUNTRY == "US")

## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...

## label_key: COUNTRY

Plot_28D

Plot_28E <- ggplot(data = First_Death_Filtered, aes(x = Date, y = log(total_rolling_average_mortality), color = COUNTRY)) + geom_line() + theme_bw() + theme(legend.title = element_blank(), legend.position = "none", axis.title.y=element_text(size=9), axis.title.x=element_text(size=9)) + ylab("Rolling Average Cumulative Mortality (log)") + gghighlight(COUNTRY == "Italy" | COUNTRY == "China" | COUNTRY == "US")

## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...

## label_key: COUNTRY

Plot_28E

countriez <- unique(Final_Data_Country$COUNTRY)[-c(8,47,59)]
country_plots<-list()

for(i in 1:length(countriez)) {
    country_plots[[i]] <- ggplot(data=subset(Final_Data_Country,COUNTRY==countriez[i]), aes_string(x="Date",y="new_rolling_average_mortality"))+geom_line(size=1)+theme_bw()+ylab("")+xlab(countriez[i])+theme(axis.text=element_blank())
}

Plot28F <- do.call("grid.arrange", c(country_plots))

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

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## Warning: Removed 10 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

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## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

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## Warning: Removed 23 row(s) containing missing values (geom_path).

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## Warning: Removed 23 row(s) containing missing values (geom_path).

## Warning: Removed 23 row(s) containing missing values (geom_path).

# Next, I create initial visualizations of the mortality growth rates by country.
Plot_29 <- plot_ly(First_Death, x=~Date, y=~total_mortality_growth) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Weekly Growth Rate in Total Rolling Average Mortality per Capita - COVID19")
Plot_29

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_30 <- plot_ly(First_Death, x=~Date, y=~total_mortality_growth_b) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Alternate Weekly Growth Rate in Total Rolling Average Mortality per Capita - COVID19")
Plot_30

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Plot_31 <- plot_ly(First_Death, x=~Date, y=~new_mortality_growth) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(title="Weekly Growth Rate in New Rolling Average Mortality per Capita - COVID19")
Plot_31

## Warning: plotly.js only supports 6 different linetypes

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

## Warning: The following are not valid linetype codes:
## 'NA'
## Valid linetypes include:
## 'solid', 'dot', 'dash', 'longdash', 'dashdot', 'longdashdot'

Pre-Estimation Visualizations

# First, we generate extensive summary statistics for each variable with describe in the Hmisc function.
Hmisc::describe(Final_Data_Country)

## Final_Data_Country 
## 
##  160  Variables      7029  Observations
## --------------------------------------------------------------------------------
## COUNTRY 
##        n  missing distinct 
##     7029        0       59 
## 
## lowest : Argentina            Australia            Austria              Belgium              Brazil              
## highest: Turkey               United Arab Emirates United Kingdom       US                   Vietnam             
## --------------------------------------------------------------------------------
## X1 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7029        0     7029        1     3515     2343    352.4    703.8 
##      .25      .50      .75      .90      .95 
##   1758.0   3515.0   5272.0   6326.2   6677.6 
## 
## lowest :    1    2    3    4    5, highest: 7025 7026 7027 7028 7029
## --------------------------------------------------------------------------------
## Date 
##          n    missing   distinct       Info       Mean        Gmd        .05 
##       7029          0        120          1 2020-02-29      39.81 2020-01-07 
##        .10        .25        .50        .75        .90        .95 
## 2020-01-13 2020-01-31 2020-03-01 2020-03-31 2020-04-17 2020-04-23 
## 
## lowest : 2020-01-01 2020-01-02 2020-01-03 2020-01-04 2020-01-05
## highest: 2020-04-25 2020-04-26 2020-04-27 2020-04-28 2020-04-29
## --------------------------------------------------------------------------------
## Total_Cases_Country 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5710     1319     2459    0.982    10837    19849        0        0 
##      .25      .50      .75      .90      .95 
##        0       98     2620    15059    57903 
## 
## lowest :       0       1       2       3       4
## highest:  905358  938154  965785  988197 1012582
## --------------------------------------------------------------------------------
## Total_Deceased_Country 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5710     1319      911    0.857    690.8     1312      0.0      0.0 
##      .25      .50      .75      .90      .95 
##      0.0      0.0     50.0    533.2   2835.2 
## 
## lowest :     0     1     2     3     4, highest: 51493 53755 54881 56259 58355
## --------------------------------------------------------------------------------
## New_Confirmed_Country 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5710     1319     1090    0.938    501.3    914.2      0.0      0.0 
##      .25      .50      .75      .90      .95 
##      0.0      8.0    133.8    791.1   2330.6 
## 
## lowest :     0     1     2     3     4, highest: 32796 33283 33755 34126 36188
## --------------------------------------------------------------------------------
## New_Total_Deceased_Country 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5710     1319      375    0.752    36.05    67.98        0        0 
##      .25      .50      .75      .90      .95 
##        0        0        4       35      144 
## 
## lowest :    0    1    2    3    4, highest: 2342 2392 2427 2472 2584
## --------------------------------------------------------------------------------
## Population 
##         n   missing  distinct      Info      Mean       Gmd       .05       .10 
##      5710      1319        59         1  92628187 144353301    625976   1886202 
##       .25       .50       .75       .90       .95 
##   5540718  19116209  67886004 145934460 331002647 
## 
## lowest :     341250     441539     625976    1207361    1326539
## highest:  212559409  273523621  331002647 1380004385 1439323774
## --------------------------------------------------------------------------------
## Total_Mortality_Rate_Per_Capita 
##           n     missing    distinct        Info        Mean         Gmd 
##        5710        1319        2120       0.857  0.00001449  0.00002677 
##         .05         .10         .25         .50         .75         .90 
## 0.000000000 0.000000000 0.000000000 0.000000000 0.000002311 0.000022685 
##         .95 
## 0.000073830 
## 
## lowest : 0.0000000000000000 0.0000000007246354 0.0000000014492708 0.0000000021739061 0.0000000028985415
## highest: 0.0005762917425392 0.0005968273668429 0.0006120996588670 0.0006218497662045 0.0006325489990350
## --------------------------------------------------------------------------------
## New_Mortality_Rate_Per_Capita 
##            n      missing     distinct         Info         Mean          Gmd 
##         5710         1319         1155        0.752 0.0000007797  0.000001421 
##          .05          .10          .25          .50          .75          .90 
## 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000001185 0.0000016293 
##          .95 
## 0.0000050630 
## 
## lowest : 0.0000000000000000 0.0000000006947707 0.0000000007246354 0.0000000013895414 0.0000000014492708
## highest: 0.0000282149123836 0.0000347725066991 0.0000359804845993 0.0000427969313220 0.0000445542910238
## --------------------------------------------------------------------------------
## Total_Cases_Country_Per_Capita 
##           n     missing    distinct        Info        Mean         Gmd 
##        5710        1319        3450       0.982   0.0003067   0.0005238 
##         .05         .10         .25         .50         .75         .90 
## 0.000000000 0.000000000 0.000000000 0.000003982 0.000171354 0.000999345 
##         .95 
## 0.001822912 
## 
## lowest : 0.0000000000000000 0.0000000007246354 0.0000000014492708 0.0000000021739061 0.0000000030211239
## highest: 0.0059027822152926 0.0059283423006633 0.0059475123646913 0.0059570973967053 0.0059762674607333
## --------------------------------------------------------------------------------
## rolling_average_confirmed 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5710     1319     1971    0.973    464.3    847.4    0.000    0.000 
##      .25      .50      .75      .90      .95 
##    0.000    7.571  120.143  719.114 2248.364 
## 
## lowest :     0.0000000     0.1428571     0.2000000     0.2500000     0.2857143
## highest: 30773.1428571 31106.5714286 31215.8571429 31308.2857143 31595.4285714
## --------------------------------------------------------------------------------
## rolling_average_deceased 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5710     1319      702    0.842    33.42    63.09    0.000    0.000 
##      .25      .50      .75      .90      .95 
##    0.000    0.000    3.143   30.043  130.550 
## 
## lowest :    0.0000000    0.1428571    0.2857143    0.4285714    0.5714286
## highest: 2117.7142857 2122.7142857 2128.1428571 2154.0000000 2201.5714286
## --------------------------------------------------------------------------------
## total_rolling_average_mortality 
##           n     missing    distinct        Info        Mean         Gmd 
##        5710        1319        2611       0.858  0.00001224  0.00002276 
##         .05         .10         .25         .50         .75         .90 
## 0.000000000 0.000000000 0.000000000 0.000000000 0.000001812 0.000017169 
##         .95 
## 0.000060324 
## 
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## new_rolling_average_mortality 
##            n      missing     distinct         Info         Mean          Gmd 
##         5710         1319         1824        0.842 0.0000007276  0.000001322 
##          .05          .10          .25          .50          .75          .90 
## 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000001293 0.0000014430 
##          .95 
## 0.0000047985 
## 
## lowest : 0.00000000000000000 0.00000000009925296 0.00000000010351934 0.00000000020703868 0.00000000031055802
## highest: 0.00002642759814352 0.00002653853488946 0.00002711787122936 0.00002799303889176 0.00002876959611333
## --------------------------------------------------------------------------------
## latitude 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5710     1319       59        1    30.98    28.87   -35.68   -14.24 
##      .25      .50      .75      .90      .95 
##    20.59    37.09    51.17    58.60    61.52 
## 
## lowest : -40.90056 -38.41610 -35.67515 -30.55948 -25.27440
## highest:  60.12816  60.47202  61.52401  61.92411  64.96305
## --------------------------------------------------------------------------------
## longitude 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5710     1319       59        1    26.04    66.37  -95.713  -71.543 
##      .25      .50      .75      .90      .95 
##    2.214   19.503   51.184  113.921  133.775 
## 
## lowest : -106.34677 -102.55278  -95.71289  -75.01515  -74.29733
## highest:  121.77402  127.76692  133.77514  138.25292  174.88597
## --------------------------------------------------------------------------------
## C1_School.closing 
##        n  missing distinct     Info     Mean      Gmd 
##     6593      436        4    0.744    1.174    1.428 
##                                   
## Value          0     1     2     3
## Frequency   3887   117   144  2445
## Proportion 0.590 0.018 0.022 0.371
## --------------------------------------------------------------------------------
## C1_Flag 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2699     4330        2    0.356     2328   0.8625   0.2372 
## 
## --------------------------------------------------------------------------------
## C2_Workplace.closing 
##        n  missing distinct     Info     Mean      Gmd 
##     6505      524        4    0.742   0.8673    1.206 
##                                   
## Value          0     1     2     3
## Frequency   4086   546   523  1350
## Proportion 0.628 0.084 0.080 0.208
## --------------------------------------------------------------------------------
## C2_Flag 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2417     4612        2    0.451     1972   0.8159   0.3006 
## 
## --------------------------------------------------------------------------------
## C3_Cancel.public.events 
##        n  missing distinct     Info     Mean      Gmd 
##     6550      479        3    0.757   0.8206   0.9662 
##                             
## Value          0     1     2
## Frequency   3723   279  2548
## Proportion 0.568 0.043 0.389
## --------------------------------------------------------------------------------
## C3_Flag 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2816     4213        2    0.467     2273   0.8072   0.3114 
## 
## --------------------------------------------------------------------------------
## C4_Restrictions.on.gatherings 
##        n  missing distinct     Info     Mean      Gmd 
##     2522     4507        5    0.888    2.065    1.933 
## 
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##                                         
## Value          0     1     2     3     4
## Frequency    944   140   224   236   978
## Proportion 0.374 0.056 0.089 0.094 0.388
## --------------------------------------------------------------------------------
## C4_Flag 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1578     5451        2    0.238     1441   0.9132   0.1587 
## 
## --------------------------------------------------------------------------------
## C5_Close.public.transport 
##        n  missing distinct     Info     Mean      Gmd 
##     6547      482        3    0.576   0.3689   0.5838 
##                             
## Value          0     1     2
## Frequency   4901   877   769
## Proportion 0.749 0.134 0.117
## --------------------------------------------------------------------------------
## C5_Flag 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1710     5319        2    0.559     1287   0.7526   0.3726 
## 
## --------------------------------------------------------------------------------
## C6_Stay.at.home.requirements 
##        n  missing distinct     Info     Mean      Gmd 
##     2425     4604        4     0.86    1.066    1.077 
##                                   
## Value          0     1     2     3
## Frequency   1033   321   950   121
## Proportion 0.426 0.132 0.392 0.050
## --------------------------------------------------------------------------------
## C6_Flag 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1392     5637        2    0.497     1100   0.7902   0.3318 
## 
## --------------------------------------------------------------------------------
## C7_Restrictions.on.internal.movement 
##        n  missing distinct     Info     Mean      Gmd 
##     6523      506        3    0.709   0.6376   0.8634 
##                             
## Value          0     1     2
## Frequency   4205   477  1841
## Proportion 0.645 0.073 0.282
## --------------------------------------------------------------------------------
## C7_Flag 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2302     4727        2    0.609     1650   0.7168   0.4062 
## 
## --------------------------------------------------------------------------------
## C8_International.travel.controls 
##        n  missing distinct     Info     Mean      Gmd 
##     6573      456        5    0.874     1.55    1.728 
## 
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##                                         
## Value          0     1     2     3     4
## Frequency   3094   415   375  1733   956
## Proportion 0.471 0.063 0.057 0.264 0.145
## --------------------------------------------------------------------------------
## E1_Income.support 
##        n  missing distinct     Info     Mean      Gmd 
##      238     6791        2    0.321   0.2437   0.4298 
##                       
## Value          0     2
## Frequency    209    29
## Proportion 0.878 0.122
## --------------------------------------------------------------------------------
## E1_Flag 
##        n  missing distinct 
##       29     7000        2 
##                       
## Value      FALSE  TRUE
## Frequency     27     2
## Proportion 0.931 0.069
## --------------------------------------------------------------------------------
## E2_Debt.contract.relief 
##        n  missing distinct     Info     Mean      Gmd 
##      240     6789        3    0.682   0.5792     0.82 
##                             
## Value          0     1     2
## Frequency    161    19    60
## Proportion 0.671 0.079 0.250
## --------------------------------------------------------------------------------
## E3_Fiscal.measures 
##          n    missing   distinct       Info       Mean        Gmd        .05 
##       6006       1023        154      0.103 1240873079 2473644332          0 
##        .10        .25        .50        .75        .90        .95 
##          0          0          0          0          0          0 
## 
## lowest :             0.00            10.75           310.70           434.00        536507.00
## highest:  420453000000.00  452656000000.00  476000000000.00  990863000000.00 1957600000000.00
## 
## 0 (5952, 0.991), 20000000000 (21, 0.003), 40000000000 (15, 0.002), 60000000000
## (3, 0.000), 80000000000 (3, 0.000), 120000000000 (2, 0.000), 200000000000 (1,
## 0.000), 220000000000 (1, 0.000), 240000000000 (2, 0.000), 360000000000 (1,
## 0.000), 420000000000 (1, 0.000), 460000000000 (1, 0.000), 480000000000 (1,
## 0.000), 1000000000000 (1, 0.000), 1960000000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 2e+10
## --------------------------------------------------------------------------------
## H1_Public.information.campaigns 
##        n  missing distinct     Info     Mean      Gmd 
##     6543      486        3    0.761    1.177   0.9667 
##                             
## Value          0     1     2
## Frequency   2542   302  3699
## Proportion 0.389 0.046 0.565
## --------------------------------------------------------------------------------
## H1_Flag 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     3992     3037        2    0.163     3762   0.9424   0.1086 
## 
## --------------------------------------------------------------------------------
## H2_Testing.policy 
##        n  missing distinct     Info     Mean      Gmd 
##     6026     1003        4    0.871   0.8432   0.9013 
##                                   
## Value          0     1     2     3
## Frequency   2475  2298   976   277
## Proportion 0.411 0.381 0.162 0.046
## --------------------------------------------------------------------------------
## H3_Contact.tracing 
##        n  missing distinct     Info     Mean      Gmd 
##     5855     1174        3    0.847   0.7643   0.8828 
##                             
## Value          0     1     2
## Frequency   2890  1455  1510
## Proportion 0.494 0.249 0.258
## --------------------------------------------------------------------------------
## H4_Emergency.investment.in.healthcare 
##         n   missing  distinct      Info      Mean       Gmd       .05       .10 
##      5799      1230        89     0.113  82850864 165312400         0         0 
##       .25       .50       .75       .90       .95 
##         0         0         0         0         0 
## 
## lowest :            0.00            0.03           35.00          562.00          585.00
## highest:   6700000000.00  14190000000.00  57458000000.00  62997723800.00 242400000000.00
## 
## 0 (5738, 0.989), 500000000 (11, 0.002), 1000000000 (3, 0.001), 1500000000 (35,
## 0.006), 2000000000 (3, 0.001), 3000000000 (1, 0.000), 4000000000 (1, 0.000),
## 4500000000 (1, 0.000), 6500000000 (2, 0.000), 14000000000 (1, 0.000),
## 57500000000 (1, 0.000), 63000000000 (1, 0.000), 242500000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 5e+08
## --------------------------------------------------------------------------------
## H5_Investment.in.vaccines 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5662     1367       25    0.046   434842   868686        0        0 
##      .25      .50      .75      .90      .95 
##        0        0        0        0        0 
## 
## lowest :         0         1       191    246961   1067577
## highest: 190372000 200000000 254591132 286175609 826000000
## 
## 0 (5638, 0.996), 2000000 (2, 0.000), 4000000 (1, 0.000), 6000000 (2, 0.000),
## 8000000 (2, 0.000), 10000000 (1, 0.000), 18000000 (1, 0.000), 26000000 (6,
## 0.001), 54000000 (1, 0.000), 100000000 (1, 0.000), 156000000 (1, 0.000),
## 174000000 (1, 0.000), 190000000 (1, 0.000), 200000000 (1, 0.000), 254000000 (1,
## 0.000), 286000000 (1, 0.000), 826000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 2000000
## --------------------------------------------------------------------------------
## StringencyIndex 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6494      535      323    0.965    31.39     36.8     0.00     0.00 
##      .25      .50      .75      .90      .95 
##     0.00    13.89    64.02    83.87    90.74 
## 
## lowest :   0.00   2.78   3.17   3.97   5.42, highest:  94.71  94.84  96.03  97.35 100.00
## --------------------------------------------------------------------------------
## LegacyStringencyIndex 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6497      532      183    0.964     34.5    39.16     0.00     0.00 
##      .25      .50      .75      .90      .95 
##     0.00    17.14    74.52    88.81    92.38 
## 
## lowest :  0.00  2.86  4.76  5.71  6.43, highest: 91.43 92.38 93.57 94.29 97.14
## --------------------------------------------------------------------------------
## Oxford_Cases 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5630     1399     2408    0.976    10841    19830        0        0 
##      .25      .50      .75      .90      .95 
##        0       83     2782    15315    60342 
## 
## lowest :       0       1       2       3       4
## highest:  890524  939053  965910  988451 1012583
## --------------------------------------------------------------------------------
## Oxford_Deaths 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5630     1399      907    0.848    687.9     1305      0.0      0.0 
##      .25      .50      .75      .90      .95 
##      0.0      0.0     53.0    542.1   2866.2 
## 
## lowest :     0     1     2     3     4, highest: 51017 53189 54876 56245 58355
## --------------------------------------------------------------------------------
## Lagged_School_Closing_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     6322      707        4    0.724    1.106    1.396 
##                                   
## Value          0     1     2     3
## Frequency   3887   105   104  2226
## Proportion 0.615 0.017 0.016 0.352
## --------------------------------------------------------------------------------
## Lagged_School_Closing_General_One_Week 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2428     4601        2    0.367     2081   0.8571   0.2451 
## 
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     6234      795        4    0.711   0.8022    1.153 
##                                   
## Value          0     1     2     3
## Frequency   4083   508   436  1207
## Proportion 0.655 0.081 0.070 0.194
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_General_One_Week 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2149     4880        2    0.462     1740   0.8097   0.3083 
## 
## --------------------------------------------------------------------------------
## Lagged_Public_Events_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     6305      724        3    0.745   0.7764   0.9483 
##                             
## Value          0     1     2
## Frequency   3723   269  2313
## Proportion 0.590 0.043 0.367
## --------------------------------------------------------------------------------
## Lagged_Public_Events_General_One_Week 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2567     4462        2    0.487     2043   0.7959    0.325 
## 
## --------------------------------------------------------------------------------
## Lagged_Gatherings_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     2296     4733        5    0.882    1.951     1.94 
## 
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##                                         
## Value          0     1     2     3     4
## Frequency    933   124   202   196   841
## Proportion 0.406 0.054 0.088 0.085 0.366
## --------------------------------------------------------------------------------
## Lagged_Gatherings_General_One_Week 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1363     5666        2    0.234     1247   0.9149   0.1558 
## 
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     6306      723        3    0.546   0.3416    0.551 
##                             
## Value          0     1     2
## Frequency   4836   786   684
## Proportion 0.767 0.125 0.108
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_General_One_Week 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1534     5495        2    0.576     1136   0.7405   0.3845 
## 
## --------------------------------------------------------------------------------
## Lagged_Lockdown_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     2194     4835        4    0.847    1.013    1.072 
##                                   
## Value          0     1     2     3
## Frequency   1004   256   835    99
## Proportion 0.458 0.117 0.381 0.045
## --------------------------------------------------------------------------------
## Lagged_Lockdown_General_One_Week 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1190     5839        2    0.518      926   0.7782   0.3456 
## 
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     6285      744        3    0.686   0.6005   0.8359 
##                             
## Value          0     1     2
## Frequency   4185   426  1674
## Proportion 0.666 0.068 0.266
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_General_One_Week 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2084     4945        2     0.63     1459   0.7001   0.4201 
## 
## --------------------------------------------------------------------------------
## Lagged_International_Travel_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     6316      713        5    0.863    1.476    1.694 
## 
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##                                         
## Value          0     1     2     3     4
## Frequency   3086   415   346  1658   811
## Proportion 0.489 0.066 0.055 0.263 0.128
## --------------------------------------------------------------------------------
## Lagged_Income_Support_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##      226     6803        2    0.264   0.1947    0.353 
##                       
## Value          0     2
## Frequency    204    22
## Proportion 0.903 0.097
## --------------------------------------------------------------------------------
## Lagged_Income_Support_General_One_Week 
##        n  missing distinct 
##       22     7007        2 
##                       
## Value      FALSE  TRUE
## Frequency     20     2
## Proportion 0.909 0.091
## --------------------------------------------------------------------------------
## Lagged_Debt_Relief_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##      226     6803        3    0.625   0.5221   0.7722 
##                             
## Value          0     1     2
## Frequency    161    12    53
## Proportion 0.712 0.053 0.235
## --------------------------------------------------------------------------------
## Lagged_Fiscal_Measures_One_Week 
##          n    missing   distinct       Info       Mean        Gmd        .05 
##       5981       1048        153      0.103 1246051988 2483935204          0 
##        .10        .25        .50        .75        .90        .95 
##          0          0          0          0          0          0 
## 
## lowest :             0.00            10.75           310.70           434.00        536507.00
## highest:  420453000000.00  452656000000.00  476000000000.00  990863000000.00 1957600000000.00
## 
## 0 (5927, 0.991), 20000000000 (21, 0.004), 40000000000 (15, 0.003), 60000000000
## (3, 0.001), 80000000000 (3, 0.001), 120000000000 (2, 0.000), 200000000000 (1,
## 0.000), 220000000000 (1, 0.000), 240000000000 (2, 0.000), 360000000000 (1,
## 0.000), 420000000000 (1, 0.000), 460000000000 (1, 0.000), 480000000000 (1,
## 0.000), 1000000000000 (1, 0.000), 1960000000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 2e+10
## --------------------------------------------------------------------------------
## Lagged_Campaign_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     6309      720        3    0.768    1.147   0.9763 
##                             
## Value          0     1     2
## Frequency   2542   296  3471
## Proportion 0.403 0.047 0.550
## --------------------------------------------------------------------------------
## Lagged_Campaign_General_One_Week 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     3758     3271        2    0.172     3528   0.9388   0.1149 
## 
## --------------------------------------------------------------------------------
## Lagged_Testing_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     5996     1033        4     0.87   0.8362   0.8947 
##                                   
## Value          0     1     2     3
## Frequency   2475  2289   971   261
## Proportion 0.413 0.382 0.162 0.044
## --------------------------------------------------------------------------------
## Lagged_Tracing_One_Week 
##        n  missing distinct     Info     Mean      Gmd 
##     5836     1193        3    0.846   0.7613   0.8814 
##                             
## Value          0     1     2
## Frequency   2889  1451  1496
## Proportion 0.495 0.249 0.256
## --------------------------------------------------------------------------------
## Lagged_Health_Care_One_Week 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5646     1383       25    0.047   436074   871145        0        0 
##      .25      .50      .75      .90      .95 
##        0        0        0        0        0 
## 
## lowest :         0         1       191    246961   1067577
## highest: 190372000 200000000 254591132 286175609 826000000
## 
## 0 (5622, 0.996), 2000000 (2, 0.000), 4000000 (1, 0.000), 6000000 (2, 0.000),
## 8000000 (2, 0.000), 10000000 (1, 0.000), 18000000 (1, 0.000), 26000000 (6,
## 0.001), 54000000 (1, 0.000), 100000000 (1, 0.000), 156000000 (1, 0.000),
## 174000000 (1, 0.000), 190000000 (1, 0.000), 200000000 (1, 0.000), 254000000 (1,
## 0.000), 286000000 (1, 0.000), 826000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 2000000
## --------------------------------------------------------------------------------
## Lagged_Stringency_Index_One_Week 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6296      733      320    0.962    29.84    35.73     0.00     0.00 
##      .25      .50      .75      .90      .95 
##     0.00    11.11    61.34    82.27    90.47 
## 
## lowest :   0.00   2.78   3.17   3.97   5.42, highest:  94.71  94.84  96.03  97.35 100.00
## --------------------------------------------------------------------------------
## Lagged_Legacy_Stringency_Index_One_Week 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6298      731      181    0.961    32.99    38.33     0.00     0.00 
##      .25      .50      .75      .90      .95 
##     0.00    14.29    70.48    87.14    92.38 
## 
## lowest :  0.00  2.86  4.76  5.71  6.43, highest: 91.43 92.38 93.57 94.29 97.14
## --------------------------------------------------------------------------------
## Lagged_School_Closing_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5932     1097        4    0.686   0.9936    1.329 
##                                   
## Value          0     1     2     3
## Frequency   3887    91    59  1895
## Proportion 0.655 0.015 0.010 0.319
## --------------------------------------------------------------------------------
## Lagged_School_Closing_General_Two_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2038     4991        2    0.382     1733   0.8503   0.2546 
## 
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5846     1183        4    0.655   0.6924    1.047 
##                                   
## Value          0     1     2     3
## Frequency   4080   457   336   973
## Proportion 0.698 0.078 0.057 0.166
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_General_Two_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1764     5265        2    0.478     1413    0.801    0.319 
## 
## --------------------------------------------------------------------------------
## Lagged_Public_Events_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5925     1104        3    0.717   0.6996    0.908 
##                             
## Value          0     1     2
## Frequency   3723   259  1943
## Proportion 0.628 0.044 0.328
## --------------------------------------------------------------------------------
## Lagged_Public_Events_General_Two_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2182     4847        2    0.516     1700   0.7791   0.3444 
## 
## --------------------------------------------------------------------------------
## Lagged_Gatherings_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     1940     5089        5    0.861     1.71    1.907 
## 
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##                                         
## Value          0     1     2     3     4
## Frequency    918    97   166   147   612
## Proportion 0.473 0.050 0.086 0.076 0.315
## --------------------------------------------------------------------------------
## Lagged_Gatherings_General_Two_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1022     6007        2    0.186      954   0.9335   0.1243 
## 
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5921     1108        3    0.491   0.2964   0.4932 
##                             
## Value          0     1     2
## Frequency   4721   645   555
## Proportion 0.797 0.109 0.094
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_General_Two_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1264     5765        2    0.601      914   0.7231   0.4008 
## 
## --------------------------------------------------------------------------------
## Lagged_Lockdown_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     1836     5193        4    0.813   0.8851    1.038 
##                                   
## Value          0     1     2     3
## Frequency    968   177   625    66
## Proportion 0.527 0.096 0.340 0.036
## --------------------------------------------------------------------------------
## Lagged_Lockdown_General_Two_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##      868     6161        2    0.543      662   0.7627   0.3624 
## 
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5901     1128        3    0.636   0.5291   0.7747 
##                             
## Value          0     1     2
## Frequency   4161   358  1382
## Proportion 0.705 0.061 0.234
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_General_Two_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1724     5305        2     0.65     1177   0.6827   0.4335 
## 
## --------------------------------------------------------------------------------
## Lagged_International_Travel_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5929     1100        5    0.842    1.356    1.628 
## 
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##                                         
## Value          0     1     2     3     4
## Frequency   3072   415   304  1536   602
## Proportion 0.518 0.070 0.051 0.259 0.102
## --------------------------------------------------------------------------------
## Lagged_Income_Support_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##      212     6817        2    0.197   0.1415   0.2642 
##                       
## Value          0     2
## Frequency    197    15
## Proportion 0.929 0.071
## --------------------------------------------------------------------------------
## Lagged_Income_Support_General_Two_Weeks 
##        n  missing distinct 
##       15     7014        2 
##                       
## Value      FALSE  TRUE
## Frequency     13     2
## Proportion 0.867 0.133
## --------------------------------------------------------------------------------
## Lagged_Debt_Relief_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##      212     6817        3    0.552   0.4575   0.7085 
##                             
## Value          0     1     2
## Frequency    161     5    46
## Proportion 0.759 0.024 0.217
## --------------------------------------------------------------------------------
## Lagged_Fiscal_Measures_Two_Weeks 
##          n    missing   distinct       Info       Mean        Gmd        .05 
##       5767       1262        137      0.096 1250385439 2492785792          0 
##        .10        .25        .50        .75        .90        .95 
##          0          0          0          0          0          0 
## 
## lowest :             0.00            10.75           310.70           434.00        536507.00
## highest:  420453000000.00  452656000000.00  476000000000.00  990863000000.00 1957600000000.00
## 
## 0 (5714, 0.991), 20000000000 (21, 0.004), 40000000000 (15, 0.003), 60000000000
## (3, 0.001), 80000000000 (3, 0.001), 120000000000 (2, 0.000), 200000000000 (1,
## 0.000), 240000000000 (2, 0.000), 360000000000 (1, 0.000), 420000000000 (1,
## 0.000), 460000000000 (1, 0.000), 480000000000 (1, 0.000), 1000000000000 (1,
## 0.000), 1960000000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 2e+10
## --------------------------------------------------------------------------------
## Lagged_Campaign_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5924     1105        3    0.778    1.094   0.9891 
##                             
## Value          0     1     2
## Frequency   2542   284  3098
## Proportion 0.429 0.048 0.523
## --------------------------------------------------------------------------------
## Lagged_Campaign_General_Two_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     3373     3656        2    0.191     3143   0.9318   0.1271 
## 
## --------------------------------------------------------------------------------
## Lagged_Testing_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5777     1252        4    0.862   0.7983   0.8687 
##                                   
## Value          0     1     2     3
## Frequency   2475  2206   882   214
## Proportion 0.428 0.382 0.153 0.037
## --------------------------------------------------------------------------------
## Lagged_Tracing_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5626     1403        3     0.84   0.7412   0.8734 
##                             
## Value          0     1     2
## Frequency   2853  1376  1397
## Proportion 0.507 0.245 0.248
## --------------------------------------------------------------------------------
## Lagged_Health_Care_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5451     1578       24    0.048   448467   895899        0        0 
##      .25      .50      .75      .90      .95 
##        0        0        0        0        0 
## 
## lowest :         0         1       191    246961   1067577
## highest: 190372000 200000000 254591132 286175609 826000000
## 
## 0 (5428, 0.996), 2000000 (2, 0.000), 4000000 (1, 0.000), 6000000 (2, 0.000),
## 8000000 (2, 0.000), 10000000 (1, 0.000), 26000000 (6, 0.001), 54000000 (1,
## 0.000), 100000000 (1, 0.000), 156000000 (1, 0.000), 174000000 (1, 0.000),
## 190000000 (1, 0.000), 200000000 (1, 0.000), 254000000 (1, 0.000), 286000000 (1,
## 0.000), 826000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 2000000
## --------------------------------------------------------------------------------
## Lagged_Stringency_Index_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5915     1114      289    0.954    26.61    33.22     0.00     0.00 
##      .25      .50      .75      .90      .95 
##     0.00    11.11    53.58    79.63    87.71 
## 
## lowest :   0.00   2.78   3.17   3.97   5.42, highest:  94.58  94.71  96.03  97.35 100.00
## --------------------------------------------------------------------------------
## Lagged_Legacy_Stringency_Index_Two_Weeks 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5917     1112      173    0.952    29.84    36.28     0.00     0.00 
##      .25      .50      .75      .90      .95 
##     0.00    14.29    63.33    84.76    90.71 
## 
## lowest :  0.00  2.86  4.76  5.71  6.43, highest: 91.43 92.38 93.57 94.29 97.14
## --------------------------------------------------------------------------------
## Lagged_School_Closing_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5543     1486        4    0.634   0.8593    1.226 
##                                   
## Value          0     1     2     3
## Frequency   3887    77    51  1528
## Proportion 0.701 0.014 0.009 0.276
## --------------------------------------------------------------------------------
## Lagged_School_Closing_General_Three_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1653     5376        2    0.409     1384   0.8373   0.2727 
## 
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5459     1570        4    0.583   0.5708   0.9098 
##                                   
## Value          0     1     2     3
## Frequency   4069   395   264   731
## Proportion 0.745 0.072 0.048 0.134
## --------------------------------------------------------------------------------
## Lagged_Workplace_Closing_General_Three_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1388     5641        2    0.513     1084    0.781   0.3423 
## 
## --------------------------------------------------------------------------------
## Lagged_Public_Events_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5543     1486        3    0.674   0.6112   0.8469 
##                             
## Value          0     1     2
## Frequency   3723   252  1568
## Proportion 0.672 0.045 0.283
## --------------------------------------------------------------------------------
## Lagged_Public_Events_General_Three_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1800     5229        2    0.554     1360   0.7556   0.3696 
## 
## --------------------------------------------------------------------------------
## Lagged_Gatherings_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     1692     5337        5    0.825    1.469    1.813 
## 
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##                                         
## Value          0     1     2     3     4
## Frequency    911    81   138   119   443
## Proportion 0.538 0.048 0.082 0.070 0.262
## --------------------------------------------------------------------------------
## Lagged_Gatherings_General_Three_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##      781     6248        2    0.183      730   0.9347   0.1222 
## 
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5541     1488        3    0.423   0.2445    0.421 
##                             
## Value          0     1     2
## Frequency   4610   507   424
## Proportion 0.832 0.091 0.077
## --------------------------------------------------------------------------------
## Lagged_Public_Transport_General_Three_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##      995     6034        2    0.627      699   0.7025   0.4184 
## 
## --------------------------------------------------------------------------------
## Lagged_Lockdown_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     1589     5440        4    0.763   0.7382   0.9554 
##                                   
## Value          0     1     2     3
## Frequency    947   147   459    36
## Proportion 0.596 0.093 0.289 0.023
## --------------------------------------------------------------------------------
## Lagged_Lockdown_General_Three_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##      642     6387        2    0.549      487   0.7586   0.3669 
## 
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5526     1503        3    0.571   0.4419   0.6852 
##                             
## Value          0     1     2
## Frequency   4143   324  1059
## Proportion 0.750 0.059 0.192
## --------------------------------------------------------------------------------
## Lagged_Internal_Movement_General_Three_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     1370     5659        2    0.678      897   0.6547   0.4524 
## 
## --------------------------------------------------------------------------------
## Lagged_International_Travel_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5542     1487        5    0.817    1.228    1.552 
## 
## lowest : 0 1 2 3 4, highest: 0 1 2 3 4
##                                         
## Value          0     1     2     3     4
## Frequency   3058   415   270  1345   454
## Proportion 0.552 0.075 0.049 0.243 0.082
## --------------------------------------------------------------------------------
## Lagged_Income_Support_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##      198     6831        2    0.116  0.08081   0.1559 
##                     
## Value         0    2
## Frequency   190    8
## Proportion 0.96 0.04
## --------------------------------------------------------------------------------
## Lagged_Income_Support_General_Three_Weeks 
##        n  missing distinct 
##        8     7021        2 
##                       
## Value      FALSE  TRUE
## Frequency      6     2
## Proportion  0.75  0.25
## --------------------------------------------------------------------------------
## Lagged_Debt_Relief_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##      198     6831        2    0.475   0.3939   0.6359 
##                       
## Value          0     2
## Frequency    159    39
## Proportion 0.803 0.197
## --------------------------------------------------------------------------------
## Lagged_Fiscal_Measures_Three_Weeks 
##          n    missing   distinct       Info       Mean        Gmd        .05 
##       5429       1600        130      0.091 1325875211 2642950468          0 
##        .10        .25        .50        .75        .90        .95 
##          0          0          0          0          0          0 
## 
## lowest :             0.00            10.75           310.70           434.00        536507.00
## highest:  420453000000.00  452656000000.00  476000000000.00  990863000000.00 1957600000000.00
## 
## 0 (5376, 0.990), 20000000000 (21, 0.004), 40000000000 (15, 0.003), 60000000000
## (3, 0.001), 80000000000 (3, 0.001), 120000000000 (2, 0.000), 200000000000 (1,
## 0.000), 240000000000 (2, 0.000), 360000000000 (1, 0.000), 420000000000 (1,
## 0.000), 460000000000 (1, 0.000), 480000000000 (1, 0.000), 1000000000000 (1,
## 0.000), 1960000000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 2e+10
## --------------------------------------------------------------------------------
## Lagged_Campaign_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5542     1487        3    0.785    1.033   0.9966 
##                             
## Value          0     1     2
## Frequency   2542   277  2723
## Proportion 0.459 0.050 0.491
## --------------------------------------------------------------------------------
## Lagged_Campaign_General_Three_Weeks 
##        n  missing distinct     Info      Sum     Mean      Gmd 
##     2991     4038        2     0.21     2765   0.9244   0.1397 
## 
## --------------------------------------------------------------------------------
## Lagged_Testing_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5414     1615        4    0.849   0.7401   0.8327 
##                                   
## Value          0     1     2     3
## Frequency   2475  2028   754   157
## Proportion 0.457 0.375 0.139 0.029
## --------------------------------------------------------------------------------
## Lagged_Tracing_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd 
##     5268     1761        3    0.824   0.7044   0.8593 
##                             
## Value          0     1     2
## Frequency   2803  1219  1246
## Proportion 0.532 0.231 0.237
## --------------------------------------------------------------------------------
## Lagged_Health_Care_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5128     1901       24    0.051   476715   952260        0        0 
##      .25      .50      .75      .90      .95 
##        0        0        0        0        0 
## 
## lowest :         0         1       191    246961   1067577
## highest: 190372000 200000000 254591132 286175609 826000000
## 
## 0 (5105, 0.996), 2000000 (2, 0.000), 4000000 (1, 0.000), 6000000 (2, 0.000),
## 8000000 (2, 0.000), 10000000 (1, 0.000), 26000000 (6, 0.001), 54000000 (1,
## 0.000), 100000000 (1, 0.000), 156000000 (1, 0.000), 174000000 (1, 0.000),
## 190000000 (1, 0.000), 200000000 (1, 0.000), 254000000 (1, 0.000), 286000000 (1,
## 0.000), 826000000 (1, 0.000)
## 
## For the frequency table, variable is rounded to the nearest 2000000
## --------------------------------------------------------------------------------
## Lagged_Stringency_Index_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5542     1487      273    0.944    23.24    30.22     0.00     0.00 
##      .25      .50      .75      .90      .95 
##     0.00    11.11    44.31    71.83    83.60 
## 
## lowest :   0.00   2.78   3.17   3.97   5.42, highest:  94.58  94.71  96.03  97.35 100.00
## --------------------------------------------------------------------------------
## Lagged_Legacy_Stringency_Index_Three_Weeks 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5543     1486      170    0.942     26.3    33.45     0.00     0.00 
##      .25      .50      .75      .90      .95 
##     0.00    14.29    53.33    80.48    88.81 
## 
## lowest :  0.00  2.86  4.76  5.71  6.43, highest: 90.71 92.38 93.57 94.29 97.14
## --------------------------------------------------------------------------------
## Latitude 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7009       20       59        1    30.81    28.97   -35.68   -14.24 
##      .25      .50      .75      .90      .95 
##    20.59    37.09    51.17    58.60    61.52 
## 
## lowest : -40.90056 -38.41610 -35.67515 -30.55948 -25.27440
## highest:  60.12816  60.47202  61.52401  61.92411  64.96305
## --------------------------------------------------------------------------------
## Longitude 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7009       20       59        1    26.14    66.82  -95.713  -71.543 
##      .25      .50      .75      .90      .95 
##    2.214   19.503   51.184  113.921  133.775 
## 
## lowest : -106.34677 -102.55278  -95.71289  -75.01515  -74.29733
## highest:  121.77402  127.76692  133.77514  138.25292  174.88597
## --------------------------------------------------------------------------------
## prop65 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7009       20       59        1    13.83    7.266    2.550    5.123 
##      .25      .50      .75      .90      .95 
##    8.088   14.795   19.379   21.462   21.954 
## 
## lowest :  1.085001  1.370070  2.550472  3.314088  3.846490
## highest: 21.655272 21.720788 21.953858 22.751680 27.576370
## --------------------------------------------------------------------------------
## propurban 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7009       20       58        1    75.82    17.61    42.70    53.73 
##      .25      .50      .75      .90      .95 
##    66.36    80.16    87.43    92.42    99.14 
## 
## lowest :  34.030  35.919  42.704  46.907  49.949
## highest:  93.813  94.612  98.001  99.135 100.000
## --------------------------------------------------------------------------------
## popdensity 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7009       20       59        1      293    429.2    4.075   15.677 
##      .25      .50      .75      .90      .95 
##   30.983  107.907  232.172  377.215  511.458 
## 
## lowest :    3.249129    3.526923    4.075308    8.822080   14.554920
## highest:  454.938073  511.457910  529.652104 1511.031250 7952.998418
## --------------------------------------------------------------------------------
## driving 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5512     1517     4248        1    82.68    39.91    20.77    30.58 
##      .25      .50      .75      .90      .95 
##    51.53    94.91   108.66   122.17   131.36 
## 
## lowest :   8.74   9.82   9.92  10.08  10.76, highest: 170.46 172.10 173.18 175.25 186.34
## --------------------------------------------------------------------------------
## walking 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5512     1517     4456        1    82.26    46.33    18.82    25.97 
##      .25      .50      .75      .90      .95 
##    45.00    91.76   110.77   130.01   143.37 
## 
## lowest :   5.82   6.27   6.30   6.67   6.96, highest: 220.80 229.51 235.37 236.75 362.50
## --------------------------------------------------------------------------------
## transit 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     2756     4273     2348        1    72.91    45.86    11.95    16.79 
##      .25      .50      .75      .90      .95 
##    28.67    91.09   106.72   117.87   125.75 
## 
## lowest :   7.04   7.16   7.33   7.58   7.61, highest: 155.72 157.63 157.64 160.26 160.56
## --------------------------------------------------------------------------------
## arrivals 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7029        0       59        1 19140656 19955221  2256000  2825000 
##      .25      .50      .75      .90      .95 
##  4425000 11196000 24551000 45768000 79745920 
## 
## lowest :  1018000  1819300  1946000  2256000  2343800
## highest: 61567200 62900000 79745920 82773000 89322000
## --------------------------------------------------------------------------------
## departures 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5949     1080       50        1 19130811 22827618  1368000  1923000 
##      .25      .50      .75      .90      .95 
##  3188000  9889000 19748000 41964000 92564000 
## 
## lowest :    667000    668000   1368000   1446000   1501000
## highest:  41964000  70386000  92564000 108542000 149720000
## --------------------------------------------------------------------------------
## vul_emp 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7029        0       59        1    17.21    15.59    1.083    4.895 
##      .25      .50      .75      .90      .95 
##    7.385   10.691   21.805   47.538   50.418 
## 
## lowest :  0.144  0.932  1.083  3.016  3.837, highest: 47.895 48.472 50.418 54.074 74.270
## --------------------------------------------------------------------------------
## GNI 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7029        0       59        1    30183    25091     2800     3840 
##      .25      .50      .75      .90      .95 
##    10230    24580    47090    61390    70870 
## 
## lowest :  2020  2360  2800  3090  3830, highest: 63080 67960 70870 80610 84410
## --------------------------------------------------------------------------------
## health_exp 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7029        0       59        1     2580     2587    115.0    161.0 
##      .25      .50      .75      .90      .95 
##    499.2   1529.1   4379.7   5800.2   7936.4 
## 
## lowest :    69.2931   105.7685   114.9718   129.5760   132.9010
## highest:  5904.5840  6086.3115  7936.3750  9956.2598 10246.1387
## --------------------------------------------------------------------------------
## pollution 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7029        0       59        1    22.45     18.9    6.185    6.732 
##      .25      .50      .75      .90      .95 
##   10.365   16.036   24.787   52.665   87.945 
## 
## lowest :  5.861331  5.956001  6.184665  6.428383  6.481147
## highest: 60.745160 86.999452 87.945447 90.873210 91.187328
## --------------------------------------------------------------------------------
## EUI_democracy 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7029        0       56        1    7.015    2.181     2.76     3.11 
##      .25      .50      .75      .90      .95 
##     6.48     7.50     8.29     9.24     9.39 
## 
## lowest : 1.93 2.26 2.76 3.06 3.08, highest: 9.25 9.26 9.39 9.58 9.87
## --------------------------------------------------------------------------------
## freedom_house 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     7029        0       37    0.998    73.56    29.39       17       21 
##      .25      .50      .75      .90      .95 
##       59       88       94       98      100 
## 
## lowest :   7  10  17  20  21, highest:  96  97  98  99 100
## --------------------------------------------------------------------------------
## cellular_sub 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       58        1    126.7    24.17    89.58    97.30 
##      .25      .50      .75      .90      .95 
##   115.53   126.20   134.93   157.43   171.61 
## 
## lowest :  86.94256  87.62149  89.58002  95.23160  95.28660
## highest: 159.93066 163.87035 171.60533 180.18261 208.50483
## --------------------------------------------------------------------------------
## milex 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       56        1 24533673 39294131       -9   329000 
##      .25      .50      .75      .90      .95 
##  1762000  4858000 13927000 56724000 61274000 
## 
## lowest :        -9     50000    256000    267000    329000
## highest:  58765000  59077000  61274000 102643000 655388000
## --------------------------------------------------------------------------------
## milper 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       53        1    216.1    301.6        5       10 
##      .25      .50      .75      .90      .95 
##       20       73      239      482     1325 
## 
## lowest :    0    1    2    5    6, highest:  511  956 1325 1569 2285
## --------------------------------------------------------------------------------
## irst 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       51    0.997    24326    41544        0        0 
##      .25      .50      .75      .90      .95 
##      805     3759     8366    42661    88695 
## 
## lowest :      0    150    300    539    632, highest:  70209  77264  88695 107232 731040
##                                                                          
## Value           0   2000   4000   6000   8000  10000  14000  16000  18000
## Frequency    1990   1320    720    840    360    120    239    120    120
## Proportion  0.288  0.191  0.104  0.122  0.052  0.017  0.035  0.017  0.017
##                                                                          
## Value       28000  34000  36000  42000  70000  78000  88000 108000 732000
## Frequency     120    120    120    120    120    120    120    120    120
## Proportion  0.017  0.017  0.017  0.017  0.017  0.017  0.017  0.017  0.017
## 
## For the frequency table, variable is rounded to the nearest 2000
## --------------------------------------------------------------------------------
## pec 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       58        1   315019   478868     2776     5653 
##      .25      .50      .75      .90      .95 
##    27817    86845   234432   468740  1385461 
## 
## lowest :     283     590    2261    2776    2937
## highest:  737482 1356742 1385461 3159873 5333707
## --------------------------------------------------------------------------------
## cinc 
##         n   missing  distinct      Info      Mean       Gmd       .05       .10 
##      6909       120        58         1   0.01385   0.02086 0.0001812 0.0003069 
##       .25       .50       .75       .90       .95 
## 0.0014276 0.0038830 0.0098926 0.0250626 0.0808987 
## 
## lowest : 0.0000308 0.0000341 0.0001778 0.0001812 0.0002811
## highest: 0.0355880 0.0400789 0.0808987 0.1393526 0.2181166
## --------------------------------------------------------------------------------
## endocrine_deaths 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     1560     5469       13    0.994     5797     8566        1        2 
##      .25      .50      .75      .90      .95 
##       98      346     8481    25482    27148 
## 
## lowest :     1     2    10    98    99, highest:  1774  8481 10564 25482 27148
##                                                                             
## Value          1     2    10    98    99   329   346  1024  1774  8481 10564
## Frequency    120   120   120   120   120   120   120   120   120   120   120
## Proportion 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077 0.077
##                       
## Value      25482 27148
## Frequency    120   120
## Proportion 0.077 0.077
## --------------------------------------------------------------------------------
## blood_pressure_deaths 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5469     1560       46        1     6759     9535        8       35 
##      .25      .50      .75      .90      .95 
##      164     2310     7559    18681    22129 
## 
## lowest :     1     4     8    14    35, highest: 18681 19729 22129 42920 74902
## --------------------------------------------------------------------------------
## respiratory_deaths 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     5589     1440       47        1    68167    92521      682      826 
##      .25      .50      .75      .90      .95 
##     3865    23856    82599   170447   201122 
## 
## lowest :    603    611    682    712    826, highest: 170447 186220 201122 463195 606604
## --------------------------------------------------------------------------------
## Lagged_Mortality_One_Week 
##           n     missing    distinct        Info        Mean         Gmd 
##        5371        1658        2338       0.838  0.00001034  0.00001936 
##         .05         .10         .25         .50         .75         .90 
## 0.000000000 0.000000000 0.000000000 0.000000000 0.000001219 0.000012357 
##         .95 
## 0.000046166 
## 
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## Lagged_Mortality_Two_Weeks 
##           n     missing    distinct        Info        Mean         Gmd 
##        4997        2032        2020       0.806 0.000008176  0.00001547 
##         .05         .10         .25         .50         .75         .90 
## 0.000000000 0.000000000 0.000000000 0.000000000 0.000000667 0.000008019 
##         .95 
## 0.000030705 
## 
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## Lagged_Mortality_Three_Weeks 
##            n      missing     distinct         Info         Mean          Gmd 
##         4590         2439         1678        0.762  0.000006605   0.00001262 
##          .05          .10          .25          .50          .75          .90 
## 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000003117 0.0000044149 
##          .95 
## 0.0000232602 
## 
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## Lagged_Mortality_Four_Weeks 
##            n      missing     distinct         Info         Mean          Gmd 
##         4181         2848         1354        0.709  0.000006095   0.00001174 
##          .05          .10          .25          .50          .75          .90 
## 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000001159 0.0000032705 
##          .95 
## 0.0000163414 
## 
## lowest : 0.0000000000000000 0.0000000001035193 0.0000000002070387 0.0000000004140774 0.0000000004315891
## highest: 0.0005225490522846 0.0005405947629573 0.0005579871794594 0.0005749851541981 0.0005914161189021
## --------------------------------------------------------------------------------
## govt_accountability 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       52    0.999   0.5407    1.065    -1.45    -1.11 
##      .25      .50      .75      .90      .95 
##    -0.01     0.86     1.38     1.61     1.62 
## 
## lowest : -1.64 -1.45 -1.28 -1.20 -1.11, highest:  1.57  1.60  1.61  1.62  1.73
## --------------------------------------------------------------------------------
## political_stability 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       52        1   0.3303   0.8518    -1.12    -0.81 
##      .25      .50      .75      .90      .95 
##    -0.28     0.43     0.92     1.29     1.41 
## 
## lowest : -1.33 -1.16 -1.12 -0.96 -0.93, highest:  1.34  1.37  1.41  1.51  1.54
## --------------------------------------------------------------------------------
## govt_effectiveness 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       54        1   0.8903   0.8495    -0.25    -0.09 
##      .25      .50      .75      .90      .95 
##     0.28     1.07     1.48     1.85     1.98 
## 
## lowest : -0.58 -0.45 -0.25 -0.21 -0.15, highest:  1.87  1.89  1.98  2.04  2.23
## --------------------------------------------------------------------------------
## regulatory_quality 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       52    0.999   0.8599   0.9027    -0.39    -0.24 
##      .25      .50      .75      .90      .95 
##     0.11     0.93     1.58     1.79     1.98 
## 
## lowest : -0.87 -0.54 -0.39 -0.31 -0.24, highest:  1.80  1.93  1.98  2.02  2.13
## --------------------------------------------------------------------------------
## rule_of_law 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       52        1   0.7956   0.9739    -0.52    -0.41 
##      .25      .50      .75      .90      .95 
##     0.02     0.97     1.63     1.88     1.93 
## 
## lowest : -0.82 -0.67 -0.52 -0.48 -0.41, highest:  1.88  1.90  1.93  1.97  2.05
## --------------------------------------------------------------------------------
## control_of_corruption 
##        n  missing distinct     Info     Mean      Gmd      .05      .10 
##     6909      120       50    0.999    0.734    1.097    -0.59    -0.49 
##      .25      .50      .75      .90      .95 
##    -0.19     0.61     1.60     2.09     2.17 
## 
## lowest : -0.86 -0.85 -0.59 -0.54 -0.49, highest:  2.09  2.14  2.15  2.17  2.21
## --------------------------------------------------------------------------------
## 
## Variables with all observations missing:
## 
## [1] M1_Wildcard

# n, nmiss, unique, mean, 5, 10, 25, 50, 75, 90, 95th percentiles

# Next, I generate shareable outputs, using the summarytools package. Set st_options(use.x11 = FALSE).
saved_x11_option <- st_options("use.x11")
st_options(use.x11 = TRUE)
dfSummary(Final_Data_Country, plain.ascii = FALSE, style = "grid", 
          graph.magnif = 0.75, valid.col = FALSE, tmp.img.dir = "/tmp")

## ### Data Frame Summary  
## #### Final_Data_Country  
## **Dimensions:** 7029 x 160  
## **Duplicates:** 0  
## 
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | No  | Variable                                      | Stats / Values                          | Freqs (% of Valid)   | Graph                | Missing  |
## +=====+===============================================+=========================================+======================+======================+==========+
## | 1   | COUNTRY\                                      | 1\. Argentina\                          | 120 ( 1.7%)\         | ![](/tmp/ds0478.png) | 0\       |
## |     | [character]                                   | 2\. Australia\                          | 120 ( 1.7%)\         |                      | (0%)     |
## |     |                                               | 3\. Austria\                            | 120 ( 1.7%)\         |                      |          |
## |     |                                               | 4\. Belgium\                            | 120 ( 1.7%)\         |                      |          |
## |     |                                               | 5\. Brazil\                             | 120 ( 1.7%)\         |                      |          |
## |     |                                               | 6\. Canada\                             | 120 ( 1.7%)\         |                      |          |
## |     |                                               | 7\. Chile\                              | 120 ( 1.7%)\         |                      |          |
## |     |                                               | 8\. China\                              | 120 ( 1.7%)\         |                      |          |
## |     |                                               | 9\. Colombia\                           | 120 ( 1.7%)\         |                      |          |
## |     |                                               | 10\. Cyprus\                            | 120 ( 1.7%)\         |                      |          |
## |     |                                               | [ 49 others ]                           | 5829 (82.9%)         |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 2   | X1\                                           | Mean (sd) : 3515 (2029.2)\              | 7029 distinct values | ![](/tmp/ds0479.png) | 0\       |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0%)     |
## |     |                                               | 1 < 3515 < 7029\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 3514 (0.6)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 3   | Date\                                         | min : 2020-01-01\                       | 120 distinct values  | ![](/tmp/ds0480.png) | 0\       |
## |     | [Date]                                        | med : 2020-03-01\                       |                      |                      | (0%)     |
## |     |                                               | max : 2020-04-29\                       |                      |                      |          |
## |     |                                               | range : 3m 28d                          |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 4   | Total_Cases_Country\                          | Mean (sd) : 10836.6 (52678.8)\          | 2459 distinct values | ![](/tmp/ds0481.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 98 < 1012582\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 2620 (4.9)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 5   | Total_Deceased_Country\                       | Mean (sd) : 690.8 (3553.1)\             | 911 distinct values  | ![](/tmp/ds0482.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 0 < 58355\                          |                      |                      |          |
## |     |                                               | IQR (CV) : 50 (5.1)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 6   | New_Confirmed_Country\                        | Mean (sd) : 501.3 (2407.7)\             | 1090 distinct values | ![](/tmp/ds0483.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 8 < 36188\                          |                      |                      |          |
## |     |                                               | IQR (CV) : 133.8 (4.8)                  |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 7   | New_Total_Deceased_Country\                   | Mean (sd) : 36 (175.2)\                 | 375 distinct values  | ![](/tmp/ds0484.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 0 < 2584\                           |                      |                      |          |
## |     |                                               | IQR (CV) : 4 (4.9)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 8   | Population\                                   | Mean (sd) : 92628187.3 (254800293.8)\   | 59 distinct values   | ![](/tmp/ds0485.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 341250 < 19116209 < 1439323774\         |                      |                      |          |
## |     |                                               | IQR (CV) : 62345286 (2.8)               |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 9   | Total_Mortality_Rate_Per_Capita\              | Mean (sd) : 0 (0)\                      | 2120 distinct values | ![](/tmp/ds0486.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (3.9)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 10  | New_Mortality_Rate_Per_Capita\                | Mean (sd) : 0 (0)\                      | 1155 distinct values | ![](/tmp/ds0487.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (3.5)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 11  | Total_Cases_Country_Per_Capita\               | Mean (sd) : 0 (0)\                      | 3450 distinct values | ![](/tmp/ds0488.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (2.6)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 12  | rolling_average_confirmed\                    | Mean (sd) : 464.3 (2262.9)\             | 1971 distinct values | ![](/tmp/ds0489.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 7.6 < 31595.4\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 120.1 (4.9)                  |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 13  | rolling_average_deceased\                     | Mean (sd) : 33.4 (164.3)\               | 702 distinct values  | ![](/tmp/ds0490.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 0 < 2201.6\                         |                      |                      |          |
## |     |                                               | IQR (CV) : 3.1 (4.9)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 14  | total_rolling_average_mortality\              | Mean (sd) : 0 (0)\                      | 2611 distinct values | ![](/tmp/ds0491.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (4.1)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 15  | new_rolling_average_mortality\                | Mean (sd) : 0 (0)\                      | 1824 distinct values | ![](/tmp/ds0492.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (3.4)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 16  | latitude\                                     | Mean (sd) : 31 (27)\                    | 59 distinct values   | ![](/tmp/ds0493.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | -40.9 < 37.1 < 65\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 30.6 (0.9)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 17  | longitude\                                    | Mean (sd) : 26 (61)\                    | 59 distinct values   | ![](/tmp/ds0494.png) | 1319\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (18.77%) |
## |     |                                               | -106.3 < 19.5 < 174.9\                  |                      |                      |          |
## |     |                                               | IQR (CV) : 49 (2.3)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 18  | C1_School.closing\                            | Mean (sd) : 1.2 (1.4)\                  | 0 : 3887 (59.0%)\    | ![](/tmp/ds0495.png) | 436\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  117 ( 1.8%)\    |                      | (6.2%)   |
## |     |                                               | 0 < 0 < 3\                              | 2 :  144 ( 2.2%)\    |                      |          |
## |     |                                               | IQR (CV) : 3 (1.2)                      | 3 : 2445 (37.1%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 19  | C1_Flag\                                      | Min  : 0\                               | 0 :  371 (13.8%)\    | ![](/tmp/ds0496.png) | 4330\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 2328 (86.2%)     |                      | (61.6%)  |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 20  | C2_Workplace.closing\                         | Mean (sd) : 0.9 (1.2)\                  | 0 : 4086 (62.8%)\    | ![](/tmp/ds0497.png) | 524\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  546 ( 8.4%)\    |                      | (7.45%)  |
## |     |                                               | 0 < 0 < 3\                              | 2 :  523 ( 8.0%)\    |                      |          |
## |     |                                               | IQR (CV) : 2 (1.4)                      | 3 : 1350 (20.8%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 21  | C2_Flag\                                      | Min  : 0\                               | 0 :  445 (18.4%)\    | ![](/tmp/ds0498.png) | 4612\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1972 (81.6%)     |                      | (65.61%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 22  | C3_Cancel.public.events\                      | Mean (sd) : 0.8 (1)\                    | 0 : 3723 (56.8%)\    | ![](/tmp/ds0499.png) | 479\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  279 ( 4.3%)\    |                      | (6.81%)  |
## |     |                                               | 0 < 0 < 2\                              | 2 : 2548 (38.9%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.2)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 23  | C3_Flag\                                      | Min  : 0\                               | 0 :  543 (19.3%)\    | ![](/tmp/ds0500.png) | 4213\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 2273 (80.7%)     |                      | (59.94%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 24  | C4_Restrictions.on.gatherings\                | Mean (sd) : 2.1 (1.8)\                  | 0 : 944 (37.4%)\     | ![](/tmp/ds0501.png) | 4507\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 140 ( 5.5%)\     |                      | (64.12%) |
## |     |                                               | 0 < 2 < 4\                              | 2 : 224 ( 8.9%)\     |                      |          |
## |     |                                               | IQR (CV) : 4 (0.9)                      | 3 : 236 ( 9.4%)\     |                      |          |
## |     |                                               |                                         | 4 : 978 (38.8%)      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 25  | C4_Flag\                                      | Min  : 0\                               | 0 :  137 ( 8.7%)\    | ![](/tmp/ds0502.png) | 5451\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 1441 (91.3%)     |                      | (77.55%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 26  | C5_Close.public.transport\                    | Mean (sd) : 0.4 (0.7)\                  | 0 : 4901 (74.9%)\    | ![](/tmp/ds0503.png) | 482\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  877 (13.4%)\    |                      | (6.86%)  |
## |     |                                               | 0 < 0 < 2\                              | 2 :  769 (11.8%)     |                      |          |
## |     |                                               | IQR (CV) : 1 (1.9)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 27  | C5_Flag\                                      | Min  : 0\                               | 0 :  423 (24.7%)\    | ![](/tmp/ds0504.png) | 5319\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1287 (75.3%)     |                      | (75.67%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 28  | C6_Stay.at.home.requirements\                 | Mean (sd) : 1.1 (1)\                    | 0 : 1033 (42.6%)\    | ![](/tmp/ds0505.png) | 4604\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  321 (13.2%)\    |                      | (65.5%)  |
## |     |                                               | 0 < 1 < 3\                              | 2 :  950 (39.2%)\    |                      |          |
## |     |                                               | IQR (CV) : 2 (0.9)                      | 3 :  121 ( 5.0%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 29  | C6_Flag\                                      | Min  : 0\                               | 0 :  292 (21.0%)\    | ![](/tmp/ds0506.png) | 5637\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1100 (79.0%)     |                      | (80.2%)  |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 30  | C7_Restrictions.on.internal.movement\         | Mean (sd) : 0.6 (0.9)\                  | 0 : 4205 (64.5%)\    | ![](/tmp/ds0507.png) | 506\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  477 ( 7.3%)\    |                      | (7.2%)   |
## |     |                                               | 0 < 0 < 2\                              | 2 : 1841 (28.2%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.4)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 31  | C7_Flag\                                      | Min  : 0\                               | 0 :  652 (28.3%)\    | ![](/tmp/ds0508.png) | 4727\    |
## |     | [numeric]                                     | Mean : 0.7\                             | 1 : 1650 (71.7%)     |                      | (67.25%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 32  | C8_International.travel.controls\             | Mean (sd) : 1.5 (1.6)\                  | 0 : 3094 (47.1%)\    | ![](/tmp/ds0509.png) | 456\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  415 ( 6.3%)\    |                      | (6.49%)  |
## |     |                                               | 0 < 1 < 4\                              | 2 :  375 ( 5.7%)\    |                      |          |
## |     |                                               | IQR (CV) : 3 (1)                        | 3 : 1733 (26.4%)\    |                      |          |
## |     |                                               |                                         | 4 :  956 (14.5%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 33  | E1_Income.support\                            | Min  : 0\                               | 0 : 209 (87.8%)\     | ![](/tmp/ds0510.png) | 6791\    |
## |     | [numeric]                                     | Mean : 0.2\                             | 2 :  29 (12.2%)      |                      | (96.61%) |
## |     |                                               | Max  : 2                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 34  | E1_Flag\                                      | 1\. FALSE\                              | 27 (93.1%)\          | ![](/tmp/ds0511.png) | 7000\    |
## |     | [logical]                                     | 2\. TRUE                                | 2 ( 6.9%)            |                      | (99.59%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 35  | E2_Debt.contract.relief\                      | Mean (sd) : 0.6 (0.9)\                  | 0 : 161 (67.1%)\     | ![](/tmp/ds0512.png) | 6789\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  19 ( 7.9%)\     |                      | (96.59%) |
## |     |                                               | 0 < 0 < 2\                              | 2 :  60 (25.0%)      |                      |          |
## |     |                                               | IQR (CV) : 1.2 (1.5)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 36  | E3_Fiscal.measures\                           | Mean (sd) : 1240873079 (31161625396.3)\ | 154 distinct values  | ![](/tmp/ds0513.png) | 1023\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (14.55%) |
## |     |                                               | 0 < 0 < 1957600000000\                  |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (25.1)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 37  | H1_Public.information.campaigns\              | Mean (sd) : 1.2 (1)\                    | 0 : 2542 (38.9%)\    | ![](/tmp/ds0514.png) | 486\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  302 ( 4.6%)\    |                      | (6.91%)  |
## |     |                                               | 0 < 2 < 2\                              | 2 : 3699 (56.5%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (0.8)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 38  | H1_Flag\                                      | Min  : 0\                               | 0 :  230 ( 5.8%)\    | ![](/tmp/ds0515.png) | 3037\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 3762 (94.2%)     |                      | (43.21%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 39  | H2_Testing.policy\                            | Mean (sd) : 0.8 (0.9)\                  | 0 : 2475 (41.1%)\    | ![](/tmp/ds0516.png) | 1003\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 2298 (38.1%)\    |                      | (14.27%) |
## |     |                                               | 0 < 1 < 3\                              | 2 :  976 (16.2%)\    |                      |          |
## |     |                                               | IQR (CV) : 1 (1)                        | 3 :  277 ( 4.6%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 40  | H3_Contact.tracing\                           | Mean (sd) : 0.8 (0.8)\                  | 0 : 2890 (49.4%)\    | ![](/tmp/ds0517.png) | 1174\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 1455 (24.9%)\    |                      | (16.7%)  |
## |     |                                               | 0 < 1 < 2\                              | 2 : 1510 (25.8%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.1)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 41  | H4_Emergency.investment.in.healthcare\        | Mean (sd) : 82850864.3 (3384818849.3)\  | 89 distinct values   | ![](/tmp/ds0518.png) | 1230\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (17.5%)  |
## |     |                                               | 0 < 0 < 242400000000\                   |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (40.9)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 42  | H5_Investment.in.vaccines\                    | Mean (sd) : 434842.1 (13135239)\        | 25 distinct values   | ![](/tmp/ds0519.png) | 1367\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (19.45%) |
## |     |                                               | 0 < 0 < 826000000\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (30.2)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 43  | M1_Wildcard\                                  | All NA's                                |                      |                      | 7029\    |
## |     | [logical]                                     |                                         |                      |                      | (100%)   |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 44  | StringencyIndex\                              | Mean (sd) : 31.4 (33.8)\                | 323 distinct values  | ![](/tmp/ds0520.png) | 535\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (7.61%)  |
## |     |                                               | 0 < 13.9 < 100\                         |                      |                      |          |
## |     |                                               | IQR (CV) : 64 (1.1)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 45  | LegacyStringencyIndex\                        | Mean (sd) : 34.5 (35.8)\                | 183 distinct values  | ![](/tmp/ds0521.png) | 532\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (7.57%)  |
## |     |                                               | 0 < 17.1 < 97.1\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 74.5 (1)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 46  | Oxford_Cases\                                 | Mean (sd) : 10841.2 (52728.7)\          | 2408 distinct values | ![](/tmp/ds0522.png) | 1399\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (19.9%)  |
## |     |                                               | 0 < 83 < 1012583\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 2781.5 (4.9)                 |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 47  | Oxford_Deaths\                                | Mean (sd) : 687.9 (3524)\               | 907 distinct values  | ![](/tmp/ds0523.png) | 1399\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (19.9%)  |
## |     |                                               | 0 < 0 < 58355\                          |                      |                      |          |
## |     |                                               | IQR (CV) : 53 (5.1)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 48  | Lagged_School_Closing_One_Week\               | Mean (sd) : 1.1 (1.4)\                  | 0 : 3887 (61.5%)\    | ![](/tmp/ds0524.png) | 707\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  105 ( 1.7%)\    |                      | (10.06%) |
## |     |                                               | 0 < 0 < 3\                              | 2 :  104 ( 1.7%)\    |                      |          |
## |     |                                               | IQR (CV) : 3 (1.3)                      | 3 : 2226 (35.2%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 49  | Lagged_School_Closing_General_One_Week\       | Min  : 0\                               | 0 :  347 (14.3%)\    | ![](/tmp/ds0525.png) | 4601\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 2081 (85.7%)     |                      | (65.46%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 50  | Lagged_Workplace_Closing_One_Week\            | Mean (sd) : 0.8 (1.2)\                  | 0 : 4083 (65.5%)\    | ![](/tmp/ds0526.png) | 795\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  508 ( 8.2%)\    |                      | (11.31%) |
## |     |                                               | 0 < 0 < 3\                              | 2 :  436 ( 7.0%)\    |                      |          |
## |     |                                               | IQR (CV) : 2 (1.5)                      | 3 : 1207 (19.4%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 51  | Lagged_Workplace_Closing_General_One_Week\    | Min  : 0\                               | 0 :  409 (19.0%)\    | ![](/tmp/ds0527.png) | 4880\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1740 (81.0%)     |                      | (69.43%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 52  | Lagged_Public_Events_One_Week\                | Mean (sd) : 0.8 (1)\                    | 0 : 3723 (59.1%)\    | ![](/tmp/ds0528.png) | 724\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  269 ( 4.3%)\    |                      | (10.3%)  |
## |     |                                               | 0 < 0 < 2\                              | 2 : 2313 (36.7%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.2)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 53  | Lagged_Public_Events_General_One_Week\        | Min  : 0\                               | 0 :  524 (20.4%)\    | ![](/tmp/ds0529.png) | 4462\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 2043 (79.6%)     |                      | (63.48%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 54  | Lagged_Gatherings_One_Week\                   | Mean (sd) : 2 (1.8)\                    | 0 : 933 (40.6%)\     | ![](/tmp/ds0530.png) | 4733\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 124 ( 5.4%)\     |                      | (67.34%) |
## |     |                                               | 0 < 2 < 4\                              | 2 : 202 ( 8.8%)\     |                      |          |
## |     |                                               | IQR (CV) : 4 (0.9)                      | 3 : 196 ( 8.5%)\     |                      |          |
## |     |                                               |                                         | 4 : 841 (36.6%)      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 55  | Lagged_Gatherings_General_One_Week\           | Min  : 0\                               | 0 :  116 ( 8.5%)\    | ![](/tmp/ds0531.png) | 5666\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 1247 (91.5%)     |                      | (80.61%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 56  | Lagged_Public_Transport_One_Week\             | Mean (sd) : 0.3 (0.7)\                  | 0 : 4836 (76.7%)\    | ![](/tmp/ds0532.png) | 723\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  786 (12.5%)\    |                      | (10.29%) |
## |     |                                               | 0 < 0 < 2\                              | 2 :  684 (10.8%)     |                      |          |
## |     |                                               | IQR (CV) : 0 (1.9)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 57  | Lagged_Public_Transport_General_One_Week\     | Min  : 0\                               | 0 :  398 (25.9%)\    | ![](/tmp/ds0533.png) | 5495\    |
## |     | [numeric]                                     | Mean : 0.7\                             | 1 : 1136 (74.1%)     |                      | (78.18%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 58  | Lagged_Lockdown_One_Week\                     | Mean (sd) : 1 (1)\                      | 0 : 1004 (45.8%)\    | ![](/tmp/ds0534.png) | 4835\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  256 (11.7%)\    |                      | (68.79%) |
## |     |                                               | 0 < 1 < 3\                              | 2 :  835 (38.1%)\    |                      |          |
## |     |                                               | IQR (CV) : 2 (1)                        | 3 :   99 ( 4.5%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 59  | Lagged_Lockdown_General_One_Week\             | Min  : 0\                               | 0 : 264 (22.2%)\     | ![](/tmp/ds0535.png) | 5839\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 926 (77.8%)      |                      | (83.07%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 60  | Lagged_Internal_Movement_One_Week\            | Mean (sd) : 0.6 (0.9)\                  | 0 : 4185 (66.6%)\    | ![](/tmp/ds0536.png) | 744\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  426 ( 6.8%)\    |                      | (10.58%) |
## |     |                                               | 0 < 0 < 2\                              | 2 : 1674 (26.6%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.5)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 61  | Lagged_Internal_Movement_General_One_Week\    | Min  : 0\                               | 0 :  625 (30.0%)\    | ![](/tmp/ds0537.png) | 4945\    |
## |     | [numeric]                                     | Mean : 0.7\                             | 1 : 1459 (70.0%)     |                      | (70.35%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 62  | Lagged_International_Travel_One_Week\         | Mean (sd) : 1.5 (1.6)\                  | 0 : 3086 (48.9%)\    | ![](/tmp/ds0538.png) | 713\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  415 ( 6.6%)\    |                      | (10.14%) |
## |     |                                               | 0 < 1 < 4\                              | 2 :  346 ( 5.5%)\    |                      |          |
## |     |                                               | IQR (CV) : 3 (1.1)                      | 3 : 1658 (26.2%)\    |                      |          |
## |     |                                               |                                         | 4 :  811 (12.8%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 63  | Lagged_Income_Support_One_Week\               | Min  : 0\                               | 0 : 204 (90.3%)\     | ![](/tmp/ds0539.png) | 6803\    |
## |     | [numeric]                                     | Mean : 0.2\                             | 2 :  22 ( 9.7%)      |                      | (96.78%) |
## |     |                                               | Max  : 2                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 64  | Lagged_Income_Support_General_One_Week\       | 1\. FALSE\                              | 20 (90.9%)\          | ![](/tmp/ds0540.png) | 7007\    |
## |     | [logical]                                     | 2\. TRUE                                | 2 ( 9.1%)            |                      | (99.69%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 65  | Lagged_Debt_Relief_One_Week\                  | Mean (sd) : 0.5 (0.8)\                  | 0 : 161 (71.2%)\     | ![](/tmp/ds0541.png) | 6803\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  12 ( 5.3%)\     |                      | (96.78%) |
## |     |                                               | 0 < 0 < 2\                              | 2 :  53 (23.4%)      |                      |          |
## |     |                                               | IQR (CV) : 1 (1.6)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 66  | Lagged_Fiscal_Measures_One_Week\              | Mean (sd) : 1246051987.5                | 153 distinct values  | ![](/tmp/ds0542.png) | 1048\    |
## |     | [numeric]                                     | (31226591440.9)\                        |                      |                      | (14.91%) |
## |     |                                               | min < med < max:\                       |                      |                      |          |
## |     |                                               | 0 < 0 < 1957600000000\                  |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (25.1)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 67  | Lagged_Campaign_One_Week\                     | Mean (sd) : 1.1 (1)\                    | 0 : 2542 (40.3%)\    | ![](/tmp/ds0543.png) | 720\     |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  296 ( 4.7%)\    |                      | (10.24%) |
## |     |                                               | 0 < 2 < 2\                              | 2 : 3471 (55.0%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (0.8)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 68  | Lagged_Campaign_General_One_Week\             | Min  : 0\                               | 0 :  230 ( 6.1%)\    | ![](/tmp/ds0544.png) | 3271\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 3528 (93.9%)     |                      | (46.54%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 69  | Lagged_Testing_One_Week\                      | Mean (sd) : 0.8 (0.8)\                  | 0 : 2475 (41.3%)\    | ![](/tmp/ds0545.png) | 1033\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 2289 (38.2%)\    |                      | (14.7%)  |
## |     |                                               | 0 < 1 < 3\                              | 2 :  971 (16.2%)\    |                      |          |
## |     |                                               | IQR (CV) : 1 (1)                        | 3 :  261 ( 4.3%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 70  | Lagged_Tracing_One_Week\                      | Mean (sd) : 0.8 (0.8)\                  | 0 : 2889 (49.5%)\    | ![](/tmp/ds0546.png) | 1193\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 1451 (24.9%)\    |                      | (16.97%) |
## |     |                                               | 0 < 1 < 2\                              | 2 : 1496 (25.6%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.1)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 71  | Lagged_Health_Care_One_Week\                  | Mean (sd) : 436074.4 (13153820.4)\      | 25 distinct values   | ![](/tmp/ds0547.png) | 1383\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (19.68%) |
## |     |                                               | 0 < 0 < 826000000\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (30.2)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 72  | Lagged_Stringency_Index_One_Week\             | Mean (sd) : 29.8 (33.1)\                | 320 distinct values  | ![](/tmp/ds0548.png) | 733\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (10.43%) |
## |     |                                               | 0 < 11.1 < 100\                         |                      |                      |          |
## |     |                                               | IQR (CV) : 61.3 (1.1)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 73  | Lagged_Legacy_Stringency_Index_One_Week\      | Mean (sd) : 33 (35.2)\                  | 181 distinct values  | ![](/tmp/ds0549.png) | 731\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (10.4%)  |
## |     |                                               | 0 < 14.3 < 97.1\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 70.5 (1.1)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 74  | Lagged_School_Closing_Two_Weeks\              | Mean (sd) : 1 (1.4)\                    | 0 : 3887 (65.5%)\    | ![](/tmp/ds0550.png) | 1097\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :   91 ( 1.5%)\    |                      | (15.61%) |
## |     |                                               | 0 < 0 < 3\                              | 2 :   59 ( 1.0%)\    |                      |          |
## |     |                                               | IQR (CV) : 3 (1.4)                      | 3 : 1895 (31.9%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 75  | Lagged_School_Closing_General_Two_Weeks\      | Min  : 0\                               | 0 :  305 (15.0%)\    | ![](/tmp/ds0551.png) | 4991\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 1733 (85.0%)     |                      | (71.01%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 76  | Lagged_Workplace_Closing_Two_Weeks\           | Mean (sd) : 0.7 (1.2)\                  | 0 : 4080 (69.8%)\    | ![](/tmp/ds0552.png) | 1183\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  457 ( 7.8%)\    |                      | (16.83%) |
## |     |                                               | 0 < 0 < 3\                              | 2 :  336 ( 5.8%)\    |                      |          |
## |     |                                               | IQR (CV) : 1 (1.7)                      | 3 :  973 (16.6%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 77  | Lagged_Workplace_Closing_General_Two_Weeks\   | Min  : 0\                               | 0 :  351 (19.9%)\    | ![](/tmp/ds0553.png) | 5265\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1413 (80.1%)     |                      | (74.9%)  |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 78  | Lagged_Public_Events_Two_Weeks\               | Mean (sd) : 0.7 (0.9)\                  | 0 : 3723 (62.8%)\    | ![](/tmp/ds0554.png) | 1104\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  259 ( 4.4%)\    |                      | (15.71%) |
## |     |                                               | 0 < 0 < 2\                              | 2 : 1943 (32.8%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.3)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 79  | Lagged_Public_Events_General_Two_Weeks\       | Min  : 0\                               | 0 :  482 (22.1%)\    | ![](/tmp/ds0555.png) | 4847\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1700 (77.9%)     |                      | (68.96%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 80  | Lagged_Gatherings_Two_Weeks\                  | Mean (sd) : 1.7 (1.8)\                  | 0 : 918 (47.3%)\     | ![](/tmp/ds0556.png) | 5089\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  97 ( 5.0%)\     |                      | (72.4%)  |
## |     |                                               | 0 < 1 < 4\                              | 2 : 166 ( 8.6%)\     |                      |          |
## |     |                                               | IQR (CV) : 4 (1)                        | 3 : 147 ( 7.6%)\     |                      |          |
## |     |                                               |                                         | 4 : 612 (31.6%)      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 81  | Lagged_Gatherings_General_Two_Weeks\          | Min  : 0\                               | 0 :  68 ( 6.7%)\     | ![](/tmp/ds0557.png) | 6007\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 954 (93.3%)      |                      | (85.46%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 82  | Lagged_Public_Transport_Two_Weeks\            | Mean (sd) : 0.3 (0.6)\                  | 0 : 4721 (79.7%)\    | ![](/tmp/ds0558.png) | 1108\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  645 (10.9%)\    |                      | (15.76%) |
## |     |                                               | 0 < 0 < 2\                              | 2 :  555 ( 9.4%)     |                      |          |
## |     |                                               | IQR (CV) : 0 (2.1)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 83  | Lagged_Public_Transport_General_Two_Weeks\    | Min  : 0\                               | 0 : 350 (27.7%)\     | ![](/tmp/ds0559.png) | 5765\    |
## |     | [numeric]                                     | Mean : 0.7\                             | 1 : 914 (72.3%)      |                      | (82.02%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 84  | Lagged_Lockdown_Two_Weeks\                    | Mean (sd) : 0.9 (1)\                    | 0 : 968 (52.7%)\     | ![](/tmp/ds0560.png) | 5193\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 177 ( 9.6%)\     |                      | (73.88%) |
## |     |                                               | 0 < 0 < 3\                              | 2 : 625 (34.0%)\     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.1)                      | 3 :  66 ( 3.6%)      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 85  | Lagged_Lockdown_General_Two_Weeks\            | Min  : 0\                               | 0 : 206 (23.7%)\     | ![](/tmp/ds0561.png) | 6161\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 662 (76.3%)      |                      | (87.65%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 86  | Lagged_Internal_Movement_Two_Weeks\           | Mean (sd) : 0.5 (0.8)\                  | 0 : 4161 (70.5%)\    | ![](/tmp/ds0562.png) | 1128\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  358 ( 6.1%)\    |                      | (16.05%) |
## |     |                                               | 0 < 0 < 2\                              | 2 : 1382 (23.4%)     |                      |          |
## |     |                                               | IQR (CV) : 1 (1.6)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 87  | Lagged_Internal_Movement_General_Two_Weeks\   | Min  : 0\                               | 0 :  547 (31.7%)\    | ![](/tmp/ds0563.png) | 5305\    |
## |     | [numeric]                                     | Mean : 0.7\                             | 1 : 1177 (68.3%)     |                      | (75.47%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 88  | Lagged_International_Travel_Two_Weeks\        | Mean (sd) : 1.4 (1.5)\                  | 0 : 3072 (51.8%)\    | ![](/tmp/ds0564.png) | 1100\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  415 ( 7.0%)\    |                      | (15.65%) |
## |     |                                               | 0 < 0 < 4\                              | 2 :  304 ( 5.1%)\    |                      |          |
## |     |                                               | IQR (CV) : 3 (1.1)                      | 3 : 1536 (25.9%)\    |                      |          |
## |     |                                               |                                         | 4 :  602 (10.2%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 89  | Lagged_Income_Support_Two_Weeks\              | Min  : 0\                               | 0 : 197 (92.9%)\     | ![](/tmp/ds0565.png) | 6817\    |
## |     | [numeric]                                     | Mean : 0.1\                             | 2 :  15 ( 7.1%)      |                      | (96.98%) |
## |     |                                               | Max  : 2                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 90  | Lagged_Income_Support_General_Two_Weeks\      | 1\. FALSE\                              | 13 (86.7%)\          | ![](/tmp/ds0566.png) | 7014\    |
## |     | [logical]                                     | 2\. TRUE                                | 2 (13.3%)            |                      | (99.79%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 91  | Lagged_Debt_Relief_Two_Weeks\                 | Mean (sd) : 0.5 (0.8)\                  | 0 : 161 (75.9%)\     | ![](/tmp/ds0567.png) | 6817\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :   5 ( 2.4%)\     |                      | (96.98%) |
## |     |                                               | 0 < 0 < 2\                              | 2 :  46 (21.7%)      |                      |          |
## |     |                                               | IQR (CV) : 0 (1.8)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 92  | Lagged_Fiscal_Measures_Two_Weeks\             | Mean (sd) : 1250385439 (31672514058.8)\ | 137 distinct values  | ![](/tmp/ds0568.png) | 1262\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (17.95%) |
## |     |                                               | 0 < 0 < 1957600000000\                  |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (25.3)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 93  | Lagged_Campaign_Two_Weeks\                    | Mean (sd) : 1.1 (1)\                    | 0 : 2542 (42.9%)\    | ![](/tmp/ds0569.png) | 1105\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  284 ( 4.8%)\    |                      | (15.72%) |
## |     |                                               | 0 < 2 < 2\                              | 2 : 3098 (52.3%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (0.9)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 94  | Lagged_Campaign_General_Two_Weeks\            | Min  : 0\                               | 0 :  230 ( 6.8%)\    | ![](/tmp/ds0570.png) | 3656\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 3143 (93.2%)     |                      | (52.01%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 95  | Lagged_Testing_Two_Weeks\                     | Mean (sd) : 0.8 (0.8)\                  | 0 : 2475 (42.8%)\    | ![](/tmp/ds0571.png) | 1252\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 2206 (38.2%)\    |                      | (17.81%) |
## |     |                                               | 0 < 1 < 3\                              | 2 :  882 (15.3%)\    |                      |          |
## |     |                                               | IQR (CV) : 1 (1)                        | 3 :  214 ( 3.7%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 96  | Lagged_Tracing_Two_Weeks\                     | Mean (sd) : 0.7 (0.8)\                  | 0 : 2853 (50.7%)\    | ![](/tmp/ds0572.png) | 1403\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 1376 (24.5%)\    |                      | (19.96%) |
## |     |                                               | 0 < 0 < 2\                              | 2 : 1397 (24.8%)     |                      |          |
## |     |                                               | IQR (CV) : 1 (1.1)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 97  | Lagged_Health_Care_Two_Weeks\                 | Mean (sd) : 448467.4 (13384823.4)\      | 24 distinct values   | ![](/tmp/ds0573.png) | 1578\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (22.45%) |
## |     |                                               | 0 < 0 < 826000000\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (29.8)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 98  | Lagged_Stringency_Index_Two_Weeks\            | Mean (sd) : 26.6 (31.3)\                | 289 distinct values  | ![](/tmp/ds0574.png) | 1114\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (15.85%) |
## |     |                                               | 0 < 11.1 < 100\                         |                      |                      |          |
## |     |                                               | IQR (CV) : 53.6 (1.2)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 99  | Lagged_Legacy_Stringency_Index_Two_Weeks\     | Mean (sd) : 29.8 (33.9)\                | 173 distinct values  | ![](/tmp/ds0575.png) | 1112\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (15.82%) |
## |     |                                               | 0 < 14.3 < 97.1\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 63.3 (1.1)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 100 | Lagged_School_Closing_Three_Weeks\            | Mean (sd) : 0.9 (1.3)\                  | 0 : 3887 (70.1%)\    | ![](/tmp/ds0576.png) | 1486\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :   77 ( 1.4%)\    |                      | (21.14%) |
## |     |                                               | 0 < 0 < 3\                              | 2 :   51 ( 0.9%)\    |                      |          |
## |     |                                               | IQR (CV) : 3 (1.6)                      | 3 : 1528 (27.6%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 101 | Lagged_School_Closing_General_Three_Weeks\    | Min  : 0\                               | 0 :  269 (16.3%)\    | ![](/tmp/ds0577.png) | 5376\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1384 (83.7%)     |                      | (76.48%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 102 | Lagged_Workplace_Closing_Three_Weeks\         | Mean (sd) : 0.6 (1.1)\                  | 0 : 4069 (74.5%)\    | ![](/tmp/ds0578.png) | 1570\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  395 ( 7.2%)\    |                      | (22.34%) |
## |     |                                               | 0 < 0 < 3\                              | 2 :  264 ( 4.8%)\    |                      |          |
## |     |                                               | IQR (CV) : 1 (1.9)                      | 3 :  731 (13.4%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 103 | Lagged_Workplace_Closing_General_Three_Weeks\ | Min  : 0\                               | 0 :  304 (21.9%)\    | ![](/tmp/ds0579.png) | 5641\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1084 (78.1%)     |                      | (80.25%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 104 | Lagged_Public_Events_Three_Weeks\             | Mean (sd) : 0.6 (0.9)\                  | 0 : 3723 (67.2%)\    | ![](/tmp/ds0580.png) | 1486\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  252 ( 4.5%)\    |                      | (21.14%) |
## |     |                                               | 0 < 0 < 2\                              | 2 : 1568 (28.3%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.5)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 105 | Lagged_Public_Events_General_Three_Weeks\     | Min  : 0\                               | 0 :  440 (24.4%)\    | ![](/tmp/ds0581.png) | 5229\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 1360 (75.6%)     |                      | (74.39%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 106 | Lagged_Gatherings_Three_Weeks\                | Mean (sd) : 1.5 (1.7)\                  | 0 : 911 (53.8%)\     | ![](/tmp/ds0582.png) | 5337\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  81 ( 4.8%)\     |                      | (75.93%) |
## |     |                                               | 0 < 0 < 4\                              | 2 : 138 ( 8.2%)\     |                      |          |
## |     |                                               | IQR (CV) : 4 (1.2)                      | 3 : 119 ( 7.0%)\     |                      |          |
## |     |                                               |                                         | 4 : 443 (26.2%)      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 107 | Lagged_Gatherings_General_Three_Weeks\        | Min  : 0\                               | 0 :  51 ( 6.5%)\     | ![](/tmp/ds0583.png) | 6248\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 730 (93.5%)      |                      | (88.89%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 108 | Lagged_Public_Transport_Three_Weeks\          | Mean (sd) : 0.2 (0.6)\                  | 0 : 4610 (83.2%)\    | ![](/tmp/ds0584.png) | 1488\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  507 ( 9.2%)\    |                      | (21.17%) |
## |     |                                               | 0 < 0 < 2\                              | 2 :  424 ( 7.6%)     |                      |          |
## |     |                                               | IQR (CV) : 0 (2.4)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 109 | Lagged_Public_Transport_General_Three_Weeks\  | Min  : 0\                               | 0 : 296 (29.8%)\     | ![](/tmp/ds0585.png) | 6034\    |
## |     | [numeric]                                     | Mean : 0.7\                             | 1 : 699 (70.2%)      |                      | (85.84%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 110 | Lagged_Lockdown_Three_Weeks\                  | Mean (sd) : 0.7 (1)\                    | 0 : 947 (59.6%)\     | ![](/tmp/ds0586.png) | 5440\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 147 ( 9.2%)\     |                      | (77.39%) |
## |     |                                               | 0 < 0 < 3\                              | 2 : 459 (28.9%)\     |                      |          |
## |     |                                               | IQR (CV) : 2 (1.3)                      | 3 :  36 ( 2.3%)      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 111 | Lagged_Lockdown_General_Three_Weeks\          | Min  : 0\                               | 0 : 155 (24.1%)\     | ![](/tmp/ds0587.png) | 6387\    |
## |     | [numeric]                                     | Mean : 0.8\                             | 1 : 487 (75.9%)      |                      | (90.87%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 112 | Lagged_Internal_Movement_Three_Weeks\         | Mean (sd) : 0.4 (0.8)\                  | 0 : 4143 (75.0%)\    | ![](/tmp/ds0588.png) | 1503\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  324 ( 5.9%)\    |                      | (21.38%) |
## |     |                                               | 0 < 0 < 2\                              | 2 : 1059 (19.2%)     |                      |          |
## |     |                                               | IQR (CV) : 1 (1.8)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 113 | Lagged_Internal_Movement_General_Three_Weeks\ | Min  : 0\                               | 0 : 473 (34.5%)\     | ![](/tmp/ds0589.png) | 5659\    |
## |     | [numeric]                                     | Mean : 0.7\                             | 1 : 897 (65.5%)      |                      | (80.51%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 114 | Lagged_International_Travel_Three_Weeks\      | Mean (sd) : 1.2 (1.5)\                  | 0 : 3058 (55.2%)\    | ![](/tmp/ds0590.png) | 1487\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  415 ( 7.5%)\    |                      | (21.16%) |
## |     |                                               | 0 < 0 < 4\                              | 2 :  270 ( 4.9%)\    |                      |          |
## |     |                                               | IQR (CV) : 3 (1.2)                      | 3 : 1345 (24.3%)\    |                      |          |
## |     |                                               |                                         | 4 :  454 ( 8.2%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 115 | Lagged_Income_Support_Three_Weeks\            | Min  : 0\                               | 0 : 190 (96.0%)\     | ![](/tmp/ds0591.png) | 6831\    |
## |     | [numeric]                                     | Mean : 0.1\                             | 2 :   8 ( 4.0%)      |                      | (97.18%) |
## |     |                                               | Max  : 2                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 116 | Lagged_Income_Support_General_Three_Weeks\    | 1\. FALSE\                              | 6 (75.0%)\           | ![](/tmp/ds0592.png) | 7021\    |
## |     | [logical]                                     | 2\. TRUE                                | 2 (25.0%)            |                      | (99.89%) |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 117 | Lagged_Debt_Relief_Three_Weeks\               | Min  : 0\                               | 0 : 159 (80.3%)\     | ![](/tmp/ds0593.png) | 6831\    |
## |     | [numeric]                                     | Mean : 0.4\                             | 2 :  39 (19.7%)      |                      | (97.18%) |
## |     |                                               | Max  : 2                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 118 | Lagged_Fiscal_Measures_Three_Weeks\           | Mean (sd) : 1325875210.9                | 130 distinct values  | ![](/tmp/ds0594.png) | 1600\    |
## |     | [numeric]                                     | (32642217375.7)\                        |                      |                      | (22.76%) |
## |     |                                               | min < med < max:\                       |                      |                      |          |
## |     |                                               | 0 < 0 < 1957600000000\                  |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (24.6)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 119 | Lagged_Campaign_Three_Weeks\                  | Mean (sd) : 1 (1)\                      | 0 : 2542 (45.9%)\    | ![](/tmp/ds0595.png) | 1487\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 :  277 ( 5.0%)\    |                      | (21.16%) |
## |     |                                               | 0 < 1 < 2\                              | 2 : 2723 (49.1%)     |                      |          |
## |     |                                               | IQR (CV) : 2 (0.9)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 120 | Lagged_Campaign_General_Three_Weeks\          | Min  : 0\                               | 0 :  226 ( 7.6%)\    | ![](/tmp/ds0596.png) | 4038\    |
## |     | [numeric]                                     | Mean : 0.9\                             | 1 : 2765 (92.4%)     |                      | (57.45%) |
## |     |                                               | Max  : 1                                |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 121 | Lagged_Testing_Three_Weeks\                   | Mean (sd) : 0.7 (0.8)\                  | 0 : 2475 (45.7%)\    | ![](/tmp/ds0597.png) | 1615\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 2028 (37.5%)\    |                      | (22.98%) |
## |     |                                               | 0 < 1 < 3\                              | 2 :  754 (13.9%)\    |                      |          |
## |     |                                               | IQR (CV) : 1 (1.1)                      | 3 :  157 ( 2.9%)     |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 122 | Lagged_Tracing_Three_Weeks\                   | Mean (sd) : 0.7 (0.8)\                  | 0 : 2803 (53.2%)\    | ![](/tmp/ds0598.png) | 1761\    |
## |     | [numeric]                                     | min < med < max:\                       | 1 : 1219 (23.1%)\    |                      | (25.05%) |
## |     |                                               | 0 < 0 < 2\                              | 2 : 1246 (23.6%)     |                      |          |
## |     |                                               | IQR (CV) : 1 (1.2)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 123 | Lagged_Health_Care_Three_Weeks\               | Mean (sd) : 476715.3 (13799516.8)\      | 24 distinct values   | ![](/tmp/ds0599.png) | 1901\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (27.05%) |
## |     |                                               | 0 < 0 < 826000000\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (28.9)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 124 | Lagged_Stringency_Index_Three_Weeks\          | Mean (sd) : 23.2 (29.2)\                | 273 distinct values  | ![](/tmp/ds0600.png) | 1487\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (21.16%) |
## |     |                                               | 0 < 11.1 < 100\                         |                      |                      |          |
## |     |                                               | IQR (CV) : 44.3 (1.3)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 125 | Lagged_Legacy_Stringency_Index_Three_Weeks\   | Mean (sd) : 26.3 (31.9)\                | 170 distinct values  | ![](/tmp/ds0601.png) | 1486\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (21.14%) |
## |     |                                               | 0 < 14.3 < 97.1\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 53.3 (1.2)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 126 | Latitude\                                     | Mean (sd) : 30.8 (27.1)\                | 59 distinct values   | ![](/tmp/ds0602.png) | 20\      |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0.28%)  |
## |     |                                               | -40.9 < 37.1 < 65\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 30.6 (0.9)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 127 | Longitude\                                    | Mean (sd) : 26.1 (61.3)\                | 59 distinct values   | ![](/tmp/ds0603.png) | 20\      |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0.28%)  |
## |     |                                               | -106.3 < 19.5 < 174.9\                  |                      |                      |          |
## |     |                                               | IQR (CV) : 49 (2.3)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 128 | prop65\                                       | Mean (sd) : 13.8 (6.4)\                 | 59 distinct values   | ![](/tmp/ds0604.png) | 20\      |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0.28%)  |
## |     |                                               | 1.1 < 14.8 < 27.6\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 11.3 (0.5)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 129 | propurban\                                    | Mean (sd) : 75.8 (15.8)\                | 58 distinct values   | ![](/tmp/ds0605.png) | 20\      |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0.28%)  |
## |     |                                               | 34 < 80.2 < 100\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 21.1 (0.2)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 130 | popdensity\                                   | Mean (sd) : 293 (1031.9)\               | 59 distinct values   | ![](/tmp/ds0606.png) | 20\      |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0.28%)  |
## |     |                                               | 3.2 < 107.9 < 7953\                     |                      |                      |          |
## |     |                                               | IQR (CV) : 201.2 (3.5)                  |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 131 | driving\                                      | Mean (sd) : 82.7 (35.2)\                | 4248 distinct values | ![](/tmp/ds0607.png) | 1517\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (21.58%) |
## |     |                                               | 8.7 < 94.9 < 186.3\                     |                      |                      |          |
## |     |                                               | IQR (CV) : 57.1 (0.4)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 132 | walking\                                      | Mean (sd) : 82.3 (40.8)\                | 4456 distinct values | ![](/tmp/ds0608.png) | 1517\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (21.58%) |
## |     |                                               | 5.8 < 91.8 < 362.5\                     |                      |                      |          |
## |     |                                               | IQR (CV) : 65.8 (0.5)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 133 | transit\                                      | Mean (sd) : 72.9 (40.7)\                | 2348 distinct values | ![](/tmp/ds0609.png) | 4273\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (60.79%) |
## |     |                                               | 7 < 91.1 < 160.6\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 78.1 (0.6)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 134 | arrivals\                                     | Mean (sd) : 19140655.6 (20601992.2)\    | 59 distinct values   | ![](/tmp/ds0610.png) | 0\       |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0%)     |
## |     |                                               | 1018000 < 11196000 < 89322000\          |                      |                      |          |
## |     |                                               | IQR (CV) : 20126000 (1.1)               |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 135 | departures\                                   | 1\. 10628000\                           | 120 ( 2.0%)\         | ![](/tmp/ds0611.png) | 1080\    |
## |     | [numeric]\                                    | 2\. 108542000\                          | 120 ( 2.0%)\         |                      | (15.36%) |
## |     | EAN-8 codes                                   | 3\. 11130000\                           | 120 ( 2.0%)\         |                      |          |
## |     |                                               | 4\. 11403000\                           | 120 ( 2.0%)\         |                      |          |
## |     |                                               | 5\. 11883000\                           | 120 ( 2.0%)\         |                      |          |
## |     |                                               | 6\. 12800000\                           | 120 ( 2.0%)\         |                      |          |
## |     |                                               | 7\. 13098000\                           | 120 ( 2.0%)\         |                      |          |
## |     |                                               | 8\. 1446000\                            | 120 ( 2.0%)\         |                      |          |
## |     |                                               | 9\. 149720000\                          | 120 ( 2.0%)\         |                      |          |
## |     |                                               | 10\. 1501000\                           | 120 ( 2.0%)\         |                      |          |
## |     |                                               | [ 40 others ]                           | 4749 (79.8%)         |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 136 | vul_emp\                                      | Mean (sd) : 17.2 (15.8)\                | 59 distinct values   | ![](/tmp/ds0612.png) | 0\       |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0%)     |
## |     |                                               | 0.1 < 10.7 < 74.3\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 14.4 (0.9)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 137 | GNI\                                          | Mean (sd) : 30183.3 (22242.4)\          | 59 distinct values   | ![](/tmp/ds0613.png) | 0\       |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0%)     |
## |     |                                               | 2020 < 24580 < 84410\                   |                      |                      |          |
## |     |                                               | IQR (CV) : 36860 (0.7)                  |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 138 | health_exp\                                   | Mean (sd) : 2579.5 (2440)\              | 59 distinct values   | ![](/tmp/ds0614.png) | 0\       |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0%)     |
## |     |                                               | 69.3 < 1529.1 < 10246.1\                |                      |                      |          |
## |     |                                               | IQR (CV) : 3880.5 (0.9)                 |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 139 | pollution\                                    | Mean (sd) : 22.4 (21.2)\                | 59 distinct values   | ![](/tmp/ds0615.png) | 0\       |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0%)     |
## |     |                                               | 5.9 < 16 < 91.2\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 14.4 (0.9)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 140 | EUI_democracy\                                | Mean (sd) : 7 (2)\                      | 56 distinct values   | ![](/tmp/ds0616.png) | 0\       |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0%)     |
## |     |                                               | 1.9 < 7.5 < 9.9\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 1.8 (0.3)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 141 | freedom_house\                                | Mean (sd) : 73.6 (28)\                  | 37 distinct values   | ![](/tmp/ds0617.png) | 0\       |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (0%)     |
## |     |                                               | 7 < 88 < 100\                           |                      |                      |          |
## |     |                                               | IQR (CV) : 35 (0.4)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 142 | cellular_sub\                                 | Mean (sd) : 126.7 (22.5)\               | 58 distinct values   | ![](/tmp/ds0618.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | 86.9 < 126.2 < 208.5\                   |                      |                      |          |
## |     |                                               | IQR (CV) : 19.4 (0.2)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 143 | milex\                                        | Mean (sd) : 24533672.9 (86338973.5)\    | 56 distinct values   | ![](/tmp/ds0619.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | -9 < 4858000 < 655388000\               |                      |                      |          |
## |     |                                               | IQR (CV) : 12165000 (3.5)               |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 144 | milper\                                       | Mean (sd) : 216.1 (402)\                | 53 distinct values   | ![](/tmp/ds0620.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | 0 < 73 < 2285\                          |                      |                      |          |
## |     |                                               | IQR (CV) : 219 (1.9)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 145 | irst\                                         | Mean (sd) : 24325.5 (96607.4)\          | 51 distinct values   | ![](/tmp/ds0621.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | 0 < 3759 < 731040\                      |                      |                      |          |
## |     |                                               | IQR (CV) : 7561 (4)                     |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 146 | pec\                                          | Mean (sd) : 315019.1 (817525.3)\        | 58 distinct values   | ![](/tmp/ds0622.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | 283 < 86845 < 5333707\                  |                      |                      |          |
## |     |                                               | IQR (CV) : 206615 (2.6)                 |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 147 | cinc\                                         | Mean (sd) : 0 (0)\                      | 58 distinct values   | ![](/tmp/ds0623.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | 0 < 0 < 0.2\                            |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (2.5)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 148 | endocrine_deaths\                             | Mean (sd) : 5796.8 (9357.1)\            | 13 distinct values   | ![](/tmp/ds0624.png) | 5469\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (77.81%) |
## |     |                                               | 1 < 346 < 27148\                        |                      |                      |          |
## |     |                                               | IQR (CV) : 8383 (1.6)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 149 | blood_pressure_deaths\                        | Mean (sd) : 6759.3 (12818.6)\           | 46 distinct values   | ![](/tmp/ds0625.png) | 1560\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (22.19%) |
## |     |                                               | 1 < 2310 < 74902\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 7395 (1.9)                   |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 150 | respiratory_deaths\                           | Mean (sd) : 68166.6 (113887.7)\         | 47 distinct values   | ![](/tmp/ds0626.png) | 1440\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (20.49%) |
## |     |                                               | 603 < 23856 < 606604\                   |                      |                      |          |
## |     |                                               | IQR (CV) : 78734 (1.7)                  |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 151 | Lagged_Mortality_One_Week\                    | Mean (sd) : 0 (0)\                      | 2338 distinct values | ![](/tmp/ds0627.png) | 1658\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (23.59%) |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (4.4)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 152 | Lagged_Mortality_Two_Weeks\                   | Mean (sd) : 0 (0)\                      | 2020 distinct values | ![](/tmp/ds0628.png) | 2032\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (28.91%) |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (5)                        |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 153 | Lagged_Mortality_Three_Weeks\                 | Mean (sd) : 0 (0)\                      | 1678 distinct values | ![](/tmp/ds0629.png) | 2439\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (34.7%)  |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (5.6)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 154 | Lagged_Mortality_Four_Weeks\                  | Mean (sd) : 0 (0)\                      | 1354 distinct values | ![](/tmp/ds0630.png) | 2848\    |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (40.52%) |
## |     |                                               | 0 < 0 < 0\                              |                      |                      |          |
## |     |                                               | IQR (CV) : 0 (6.1)                      |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 155 | govt_accountability\                          | Mean (sd) : 0.5 (1)\                    | 52 distinct values   | ![](/tmp/ds0631.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | -1.6 < 0.9 < 1.7\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 1.4 (1.8)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 156 | political_stability\                          | Mean (sd) : 0.3 (0.7)\                  | 52 distinct values   | ![](/tmp/ds0632.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | -1.3 < 0.4 < 1.5\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 1.2 (2.3)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 157 | govt_effectiveness\                           | Mean (sd) : 0.9 (0.7)\                  | 54 distinct values   | ![](/tmp/ds0633.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | -0.6 < 1.1 < 2.2\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 1.2 (0.8)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 158 | regulatory_quality\                           | Mean (sd) : 0.9 (0.8)\                  | 52 distinct values   | ![](/tmp/ds0634.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | -0.9 < 0.9 < 2.1\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 1.5 (0.9)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 159 | rule_of_law\                                  | Mean (sd) : 0.8 (0.8)\                  | 52 distinct values   | ![](/tmp/ds0635.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | -0.8 < 1 < 2\                           |                      |                      |          |
## |     |                                               | IQR (CV) : 1.6 (1.1)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+
## | 160 | control_of_corruption\                        | Mean (sd) : 0.7 (1)\                    | 50 distinct values   | ![](/tmp/ds0636.png) | 120\     |
## |     | [numeric]                                     | min < med < max:\                       |                      |                      | (1.71%)  |
## |     |                                               | -0.9 < 0.6 < 2.2\                       |                      |                      |          |
## |     |                                               | IQR (CV) : 1.8 (1.3)                    |                      |                      |          |
## +-----+-----------------------------------------------+-----------------------------------------+----------------------+----------------------+----------+

view(dfSummary(Final_Data_Country))

## Switching method to 'browser'

## Output file written: /var/folders/gp/rp10rtns73j6fmfn3240876c0000gn/T//Rtmp8riyG1/filea86965b4252e.html

# For the complete output, please open the summary in the html link.

# Now, I generate a correlation heat map, using a subset of variables from the final dataset.
corrmapvarlist <- c("total_rolling_average_mortality", "Total_Cases_Country", "Total_Deceased_Country", "New_Confirmed_Country", "New_Total_Deceased_Country", "Population", "rolling_average_confirmed", "rolling_average_deceased", "C1_School.closing", "C2_Workplace.closing", "C3_Cancel.public.events", "C4_Restrictions.on.gatherings", "C5_Close.public.transport", "C6_Stay.at.home.requirements", "C7_Restrictions.on.internal.movement", "C8_International.travel.controls", "E1_Income.support", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H1_Public.information.campaigns", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare", "H5_Investment.in.vaccines", "StringencyIndex", "LegacyStringencyIndex", "prop65", "propurban", "driving", "walking")
corrmapvars <- Final_Data_Country[corrmapvarlist]

# Here, I rename the variables to make them easier to read on the map.
corrmapvars <- corrmapvars %>% dplyr::rename(mortality_ra = total_rolling_average_mortality)
corrmapvars <- corrmapvars %>% dplyr::rename(confirmed_ra = rolling_average_confirmed)
corrmapvars <- corrmapvars %>% dplyr::rename(deceased_ra = rolling_average_deceased)
corrmapvars <- corrmapvars %>% dplyr::rename(cases = Total_Cases_Country)
corrmapvars <- corrmapvars %>% dplyr::rename(deaths = Total_Deceased_Country)
corrmapvars <- corrmapvars %>% dplyr::rename(new_cases = New_Confirmed_Country)
corrmapvars <- corrmapvars %>% dplyr::rename(new_deaths = New_Total_Deceased_Country)
corrmapvars <- corrmapvars %>% dplyr::rename(school_closing = C1_School.closing)
corrmapvars <- corrmapvars %>% dplyr::rename(work_closing = C2_Workplace.closing)
corrmapvars <- corrmapvars %>% dplyr::rename(public_event_closing = C3_Cancel.public.events)
corrmapvars <- corrmapvars %>% dplyr::rename(gathering_restrictions = C4_Restrictions.on.gatherings)
corrmapvars <- corrmapvars %>% dplyr::rename(transport_closing = C5_Close.public.transport)
corrmapvars <- corrmapvars %>% dplyr::rename(lockdown = C6_Stay.at.home.requirements)
corrmapvars <- corrmapvars %>% dplyr::rename(internal_restrictions = C7_Restrictions.on.internal.movement)
corrmapvars <- corrmapvars %>% dplyr::rename(travel_restrictions = C8_International.travel.controls)
corrmapvars <- corrmapvars %>% dplyr::rename(income = E1_Income.support)
corrmapvars <- corrmapvars %>% dplyr::rename(debt = E2_Debt.contract.relief)
corrmapvars <- corrmapvars %>% dplyr::rename(fiscal = E3_Fiscal.measures)
corrmapvars <- corrmapvars %>% dplyr::rename(campaigns = H1_Public.information.campaigns)
corrmapvars <- corrmapvars %>% dplyr::rename(testing = H2_Testing.policy)
corrmapvars <- corrmapvars %>% dplyr::rename(tracing = H3_Contact.tracing)
corrmapvars <- corrmapvars %>% dplyr::rename(health_care = H4_Emergency.investment.in.healthcare)
corrmapvars <- corrmapvars %>% dplyr::rename(vaccine = H5_Investment.in.vaccines)
corrmapvars <- corrmapvars %>% dplyr::rename(stringency_index = StringencyIndex)
corrmapvars <- corrmapvars %>% dplyr::rename(legacy_stringency_index = LegacyStringencyIndex)
corrmapvalues <- round(cor(corrmapvars, use = "pairwise.complete.obs"), 2)

# Here, I get the lower triangle of the correlation matrix
get_lower_tri<-function(cormat){
  cormat[upper.tri(cormat)] <- NA
  return(cormat)
}
# Now, I get the upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
  cormat[lower.tri(cormat)]<- NA
  return(cormat)
}
reorder_cormat <- function(cormat){
### Use correlation between variables as distance
dd <- as.dist((1-cormat)/2)
hc <- hclust(dd)
cormat <-cormat[hc$order, hc$order]
}
### Reorder the correlation matrix
corrmapvalues <- reorder_cormat(corrmapvalues)
### Get the Upper Triangle
upper_tri <- get_upper_tri(corrmapvalues) 
### Melt the Correlation Map
melted_corrmap <- reshape2::melt(upper_tri, na.rm = TRUE)

### Generate the Static Correlation Map
ggheatmap <- ggplot(data = melted_corrmap, aes(x=Var2, y=Var1, fill=value)) + 
  geom_tile(color = "white") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white",
    midpoint = 0, limit = c(-1,1), space = "Lab", 
    name="Pearson\nCorrelation") +
  theme(axis.text.x = element_text(angle = 45)) +
  theme(axis.text.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0))) +
  theme(axis.text.x = element_text(hjust=1))
print(ggheatmap)

### Generate an Interactive Correlation Map (you are able to zoom to specific correlates).
interactive_heatmap <- heatmaply_cor(
  cor(corrmapvalues),
  row_text_angle = 0,
  column_text_angle = 45,
  na.value = "grey50",
  na.rm = TRUE,
  xlab = "Features",
  ylab = "Features",
  k_col = 0,
  k_row = 0,
  fontsize_row = 5,
  fontsize_col = 5,
  plot_method = c("ggplot", "plotly"),
  show_dendrogram = c(FALSE, FALSE),
  key.title = "Pearson Correlations")
interactive_heatmap

## Following code from: https://www.rstatisticsblog.com/data-science-in-action/lasso-regression/.

str(Final_Data_Country)

## 'data.frame':    7029 obs. of  160 variables:
##  $ COUNTRY                                     : chr  "Argentina" "Argentina" "Argentina" "Argentina" ...
##  $ X1                                          : num  119 120 112 104 23 22 12 3 1 26 ...
##  $ Date                                        : Date, format: "2020-01-01" "2020-01-02" ...
##  $ Total_Cases_Country                         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Total_Deceased_Country                      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ New_Confirmed_Country                       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ New_Total_Deceased_Country                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Population                                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Total_Mortality_Rate_Per_Capita             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ New_Mortality_Rate_Per_Capita               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Total_Cases_Country_Per_Capita              : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ rolling_average_confirmed                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ rolling_average_deceased                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_rolling_average_mortality             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_rolling_average_mortality               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ latitude                                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ longitude                                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C1_School.closing                           : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ C1_Flag                                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C2_Workplace.closing                        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ C2_Flag                                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C3_Cancel.public.events                     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ C3_Flag                                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C4_Restrictions.on.gatherings               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C4_Flag                                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C5_Close.public.transport                   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ C5_Flag                                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C6_Stay.at.home.requirements                : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C6_Flag                                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C7_Restrictions.on.internal.movement        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ C7_Flag                                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ C8_International.travel.controls            : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ E1_Income.support                           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ E1_Flag                                     : logi  NA NA NA NA NA NA ...
##  $ E2_Debt.contract.relief                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ E3_Fiscal.measures                          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ H1_Public.information.campaigns             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ H1_Flag                                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ H2_Testing.policy                           : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ H3_Contact.tracing                          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ H4_Emergency.investment.in.healthcare       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ H5_Investment.in.vaccines                   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ M1_Wildcard                                 : logi  NA NA NA NA NA NA ...
##  $ StringencyIndex                             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ LegacyStringencyIndex                       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Oxford_Cases                                : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Oxford_Deaths                               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_School_Closing_One_Week              : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_School_Closing_General_One_Week      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Workplace_Closing_One_Week           : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Workplace_Closing_General_One_Week   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Public_Events_One_Week               : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Public_Events_General_One_Week       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Gatherings_One_Week                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Gatherings_General_One_Week          : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Public_Transport_One_Week            : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Public_Transport_General_One_Week    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Lockdown_One_Week                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Lockdown_General_One_Week            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Internal_Movement_One_Week           : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Internal_Movement_General_One_Week   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_International_Travel_One_Week        : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Income_Support_One_Week              : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Income_Support_General_One_Week      : logi  NA NA NA NA NA NA ...
##  $ Lagged_Debt_Relief_One_Week                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Fiscal_Measures_One_Week             : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Campaign_One_Week                    : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Campaign_General_One_Week            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Testing_One_Week                     : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Tracing_One_Week                     : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Health_Care_One_Week                 : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Stringency_Index_One_Week            : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_Legacy_Stringency_Index_One_Week     : num  NA NA NA NA NA NA NA 0 0 0 ...
##  $ Lagged_School_Closing_Two_Weeks             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_School_Closing_General_Two_Weeks     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Workplace_Closing_Two_Weeks          : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Workplace_Closing_General_Two_Weeks  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Public_Events_Two_Weeks              : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Public_Events_General_Two_Weeks      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Gatherings_Two_Weeks                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Gatherings_General_Two_Weeks         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Public_Transport_Two_Weeks           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Public_Transport_General_Two_Weeks   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Lockdown_Two_Weeks                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Lockdown_General_Two_Weeks           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Internal_Movement_Two_Weeks          : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Internal_Movement_General_Two_Weeks  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_International_Travel_Two_Weeks       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Income_Support_Two_Weeks             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Income_Support_General_Two_Weeks     : logi  NA NA NA NA NA NA ...
##  $ Lagged_Debt_Relief_Two_Weeks                : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Fiscal_Measures_Two_Weeks            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Campaign_Two_Weeks                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Campaign_General_Two_Weeks           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Testing_Two_Weeks                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Tracing_Two_Weeks                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Health_Care_Two_Weeks                : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Stringency_Index_Two_Weeks           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Lagged_Legacy_Stringency_Index_Two_Weeks    : num  NA NA NA NA NA NA NA NA NA NA ...
##   [list output truncated]

# The following code gives us every set of possible relationships between our numeric independent variables. Factor variables and lags have been excluded.

# First, government responses linked to the seven-day rolling average of mortality rates per capita.
pairs.panels(Final_Data_Country[c("total_rolling_average_mortality", "StringencyIndex", "E1_Income.support", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare")])

# Next, government responses linked to the total and new mortality rates per capita.
pairs.panels(Final_Data_Country[c("Total_Mortality_Rate_Per_Capita", "StringencyIndex", "E1_Income.support", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare")])

pairs.panels(Final_Data_Country[c("New_Mortality_Rate_Per_Capita", "StringencyIndex", "E1_Income.support", "E2_Debt.contract.relief", "E3_Fiscal.measures", "H2_Testing.policy", "H3_Contact.tracing", "H4_Emergency.investment.in.healthcare")])

CUMULATIVE MORTALITY RATES - Cross-Sectional + Panel Analysis (Appendix Results)

library(lubridate)
library(zoo)
library(quantmod)
library(fBasics)
library(tseries)
library(sandwich)
library(lmtest)
library(lattice)
library(xtable)

## 
## Attaching package: 'xtable'

## The following objects are masked from 'package:Hmisc':
## 
##     label, label<-

## The following object is masked from 'package:timeSeries':
## 
##     align

## The following object is masked from 'package:timeDate':
## 
##     align

## The following objects are masked from 'package:summarytools':
## 
##     label, label<-

library(vars)
library(plyr)

## ------------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## ------------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:Hmisc':
## 
##     is.discrete, summarize

## The following objects are masked from 'package:plotly':
## 
##     arrange, mutate, rename, summarise

## The following objects are masked from 'package:reshape':
## 
##     rename, round_any

## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following object is masked from 'package:purrr':
## 
##     compact

library(gridExtra)
library(corrplot)
library(ggplot2)
library(reshape2)

## Warning: package 'reshape2' was built under R version 3.6.2

## 
## Attaching package: 'reshape2'

## The following objects are masked from 'package:data.table':
## 
##     dcast, melt

## The following objects are masked from 'package:reshape':
## 
##     colsplit, melt, recast

## The following object is masked from 'package:tidyr':
## 
##     smiths

library(data.table)
library(rvest)

## Loading required package: xml2

## Warning: package 'xml2' was built under R version 3.6.2

## 
## Attaching package: 'xml2'

## The following object is masked from 'package:rlang':
## 
##     as_list

## 
## Attaching package: 'rvest'

## The following object is masked from 'package:Hmisc':
## 
##     html

## The following object is masked from 'package:purrr':
## 
##     pluck

## The following object is masked from 'package:readr':
## 
##     guess_encoding

library(plm)
library(tidyverse)
library(stringr)
library(stargazer)
library(haven)

Merge global preference survey data.

dat<-Final_Data_Country
# Remove factor variables for government responses and lagged variables
dat<-dat %>% dplyr::select(-contains("Flag"))
dat<-dat %>% dplyr::select(-contains("Lagged"))
dat$latitude <- NULL
dat$longitude <- NULL

gps.country <- read_dta("data/global_preference_survey_country.dta")
# Change country names for merge
gps.country[which(gps.country$country == "South Korea"),"country"] <- "Korea, South"
gps.country[which(gps.country$country == "Czech Republic"),"country"] <- "Czechia"
gps.country[which(gps.country$country == "United States"),"country"] <- "US"

gps.individual <- read_dta("data/global_preference_survey_individual.dta")
# Change country names for merge
gps.individual[which(gps.individual$country == "South Korea"),"country"] <- "Korea, South"
gps.individual[which(gps.individual$country == "Czech Republic"),"country"] <- "Czechia"
gps.individual[which(gps.individual$country == "United States"),"country"] <- "US"

gps.individual$age <- as.character(gps.individual$age)
gps.individual[which(gps.individual$age == "99 99+"), "age"] <- "99"
gps.individual[which(gps.individual$age == "100 (Refused"), "age"] <- NA
gps.individual$age <- as.numeric(gps.individual$age)
gps.individual.pop65 <- gps.individual[which(!is.na(gps.individual$age) & gps.individual$age > 65), ]
gps.individual.pop65 <- gps.individual.pop65[which(!is.na(gps.individual.pop65$trust)),]
gps.country.pop65 <- ddply(gps.individual.pop65, "country", function(X) data.frame(wm.trust = weighted.mean(X$trust, X$wgt)))

colnames(gps.country)[1] <- "COUNTRY"
dat <- merge(dat, gps.country, by=c("COUNTRY"), all.x=TRUE)
colnames(gps.country.pop65)[1] <- "COUNTRY"
dat <- merge(dat, gps.country.pop65, by=c("COUNTRY"), all.x=TRUE)

Rename variables and create some new variables

p.dat_cum<-pdata.frame(dat, index = c("COUNTRY", "Date"))
p.dat_cum$Date<-as.Date(p.dat_cum$Date,"%Y-%m-%d")
#reorder
p.dat_cum<-p.dat_cum[order(p.dat_cum$Date),]
p.dat_cum<-p.dat_cum[order(p.dat_cum$COUNTRY),]

colnames(p.dat_cum)[colnames(p.dat_cum) == "Total_Cases_Country"] <- "Total_Case_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "Total_Deceased_Country"] <- "Total_Death_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "New_Confirmed_Country"] <- "New_Case_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "New_Total_Deceased_Country"] <- "New_Death_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "Total_Mortality_Rate_Per_Capita"] <- "Total_Mortality_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "New_Mortality_Rate_Per_Capita"] <- "New_Mortality_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "Total_Cases_Country_Per_Capita"] <- "Total_Case_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "rolling_average_confirmed"] <- "RollingAverage_New_Case_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "rolling_average_deceased"] <- "RollingAverage_New_Death_Country"
colnames(p.dat_cum)[colnames(p.dat_cum) == "total_rolling_average_mortality"] <- "RollingAverage_Total_Mortality_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "new_rolling_average_mortality"] <- "RollingAverage_New_Mortality_Rate"
colnames(p.dat_cum)[colnames(p.dat_cum) == "EUI_democracy"] <- "EIU_Democracy"
p.dat_cum$RollingAverage_Total_Mortality_Rate <- NULL
p.dat_cum$longitude <- NULL

p.dat_cum$New_Case_Rate = p.dat_cum$New_Case_Country/p.dat_cum$Population

p.dat_cum <- p.dat_cum %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(RollingAverage_New_Case_Rate = frollmean(New_Case_Rate, 7,na.rm=TRUE))

p.dat_cum <- p.dat_cum %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(Total_Mortality_Rate_Growth = log(Total_Mortality_Rate) - Lag(log(Total_Mortality_Rate),7))

p.dat_cum <- p.dat_cum %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(Total_Case_Rate_Growth = log(Total_Case_Rate) - Lag(log(Total_Case_Rate),7))

p.dat_cum <- p.dat_cum %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(New_Case_Rate_Growth = log(RollingAverage_New_Case_Rate) - Lag(log(RollingAverage_New_Case_Rate),7))

p.dat_cum <- p.dat_cum %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(New_Mortality_Rate_Growth = log(RollingAverage_New_Mortality_Rate) - Lag(log(RollingAverage_New_Mortality_Rate),7))

p.dat_cum$respiratory_deaths_rate <- 1/3*(p.dat_cum$respiratory_deaths/p.dat_cum$Population)

Reorder columns

p.dat_cum <- p.dat_cum %>%
 dplyr::select(c("COUNTRY", "X1", "Date",
          "Total_Case_Country", "Total_Death_Country", "New_Case_Country", "New_Death_Country", "Population",
          "Total_Case_Rate", "Total_Mortality_Rate", "New_Case_Rate", "New_Mortality_Rate", 
          "RollingAverage_New_Case_Country", "RollingAverage_New_Death_Country", 
          "RollingAverage_New_Case_Rate", "RollingAverage_New_Mortality_Rate", 
          "Total_Case_Rate_Growth", "Total_Mortality_Rate_Growth", 
          "New_Case_Rate_Growth", "New_Mortality_Rate_Growth"), everything())

p.dat_cum$Total_Case_Rate_Growth[is.nan(p.dat_cum$Total_Case_Rate_Growth)] <- NA
p.dat_cum$Total_Mortality_Rate_Growth[is.nan(p.dat_cum$Total_Mortality_Rate_Growth)] <- NA

p.dat_cum$New_Case_Rate_Growth[is.nan(p.dat_cum$New_Case_Rate_Growth)] <- NA
p.dat_cum$New_Mortality_Rate_Growth[is.nan(p.dat_cum$New_Mortality_Rate_Growth)] <- NA

p.dat_cum$New_Mortality_Rate_Growth[is.infinite(-p.dat_cum$New_Mortality_Rate_Growth)] <- Inf

colnames(p.dat_cum)

##  [1] "COUNTRY"                              
##  [2] "X1"                                   
##  [3] "Date"                                 
##  [4] "Total_Case_Country"                   
##  [5] "Total_Death_Country"                  
##  [6] "New_Case_Country"                     
##  [7] "New_Death_Country"                    
##  [8] "Population"                           
##  [9] "Total_Case_Rate"                      
## [10] "Total_Mortality_Rate"                 
## [11] "New_Case_Rate"                        
## [12] "New_Mortality_Rate"                   
## [13] "RollingAverage_New_Case_Country"      
## [14] "RollingAverage_New_Death_Country"     
## [15] "RollingAverage_New_Case_Rate"         
## [16] "RollingAverage_New_Mortality_Rate"    
## [17] "Total_Case_Rate_Growth"               
## [18] "Total_Mortality_Rate_Growth"          
## [19] "New_Case_Rate_Growth"                 
## [20] "New_Mortality_Rate_Growth"            
## [21] "C1_School.closing"                    
## [22] "C2_Workplace.closing"                 
## [23] "C3_Cancel.public.events"              
## [24] "C4_Restrictions.on.gatherings"        
## [25] "C5_Close.public.transport"            
## [26] "C6_Stay.at.home.requirements"         
## [27] "C7_Restrictions.on.internal.movement" 
## [28] "C8_International.travel.controls"     
## [29] "E1_Income.support"                    
## [30] "E2_Debt.contract.relief"              
## [31] "E3_Fiscal.measures"                   
## [32] "H1_Public.information.campaigns"      
## [33] "H2_Testing.policy"                    
## [34] "H3_Contact.tracing"                   
## [35] "H4_Emergency.investment.in.healthcare"
## [36] "H5_Investment.in.vaccines"            
## [37] "M1_Wildcard"                          
## [38] "StringencyIndex"                      
## [39] "LegacyStringencyIndex"                
## [40] "Oxford_Cases"                         
## [41] "Oxford_Deaths"                        
## [42] "Latitude"                             
## [43] "Longitude"                            
## [44] "prop65"                               
## [45] "propurban"                            
## [46] "popdensity"                           
## [47] "driving"                              
## [48] "walking"                              
## [49] "transit"                              
## [50] "arrivals"                             
## [51] "departures"                           
## [52] "vul_emp"                              
## [53] "GNI"                                  
## [54] "health_exp"                           
## [55] "pollution"                            
## [56] "EIU_Democracy"                        
## [57] "freedom_house"                        
## [58] "cellular_sub"                         
## [59] "milex"                                
## [60] "milper"                               
## [61] "irst"                                 
## [62] "pec"                                  
## [63] "cinc"                                 
## [64] "endocrine_deaths"                     
## [65] "blood_pressure_deaths"                
## [66] "respiratory_deaths"                   
## [67] "govt_accountability"                  
## [68] "political_stability"                  
## [69] "govt_effectiveness"                   
## [70] "regulatory_quality"                   
## [71] "rule_of_law"                          
## [72] "control_of_corruption"                
## [73] "isocode"                              
## [74] "patience"                             
## [75] "risktaking"                           
## [76] "posrecip"                             
## [77] "negrecip"                             
## [78] "altruism"                             
## [79] "trust"                                
## [80] "wm.trust"                             
## [81] "respiratory_deaths_rate"

Baseline regressions - Cumulative Mortality

p.dat_cum <- p.dat_cum %>% ungroup()
p.dat_cum <- pdata.frame(p.dat_cum, index = c("COUNTRY", "Date"))
p.dat_cum <- p.dat_cum[order(p.dat_cum$Date),]
p.dat_cum <- p.dat_cum[order(p.dat_cum$COUNTRY),]

###baseline projection regressions, remove 'infinite' values. two-way FE, HAC robust SEs, clustered on country
regmoda<-plm(Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))

regmodb<-plm(Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))

regmodc<-plm(Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))

regmodd<-plm(Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))

HTML output of baseline regression results - Cumulative Mortality

stargazer(digits=6,regmoda,regmodb,regmodc,regmodd,type="html",se=list(se.a[,2],se.b[,2],se.c[,2],se.d[,2]),out=("Table_Appendix_baseline_panel_output.htm"),
          dep.var.labels=c("Cumulative Mortality Growth (t)"),
          covariate.labels=c("Cum. Mortality Growth (t-7)","Stringency (t-14)","Cum. Mortality Growth (t-14)","Stringency (t-21)","Cum. Mortality Growth (t-21)","Stringency (t-28)","Cum. Mortality Growth (t-28)","Stringency (t-35)"), df = FALSE,omit.stat="adj.rsq",notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","HAC robust standard errors, clustered by country. Time and Country FEs."),notes.append=F,notes.align ="l",title="Local Projection Regressions (Appendix)",add.lines = list(c("Fixed effects?", "Y", "Y","Y","Y")))

Plots - Cumulative Mortality

### plot of 10-unit increase in Stringency effects over time on future mortality growth
p.dat<-c(coef(regmoda)[2],coef(regmodb)[2],coef(regmodc)[2],coef(regmodd)[2])
p.dat<-cbind(p.dat,p.dat+1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
p.dat<-cbind(p.dat,p.dat[,1]-1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
colnames(p.dat)<-c("mean","upper","lower")
p.dat<-data.frame(p.dat)*10*100

Plot_32 <-ggplot(data=p.dat,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean))+geom_point(size=4,shape=21,fill="grey")+geom_hline(yintercept=0,linetype=2)+xlab("Increase in Stringency Index of 10 units")+theme_bw()+ylab("Effect on Cum. Mortality Growth (%)")+geom_errorbar(aes(ymin=lower,ymax=upper))
Plot_32                                                                               

Plot_33 <-ggplot(data=p.dat,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean))+geom_point(size=4,shape=21,fill="grey")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cum. Mortality Growth (%)")+geom_errorbar(aes(ymin=lower,ymax=upper))+ggtitle("Average Impact")+ylim(-15,5)
Plot_33

Loop to generate heterogeneity regression results

###baseline projection regressions, remove 'infinite' values. two-way FE, HAC robust SEs, clustered on country
var.list <- c("prop65", "propurban", "Latitude", "Longitude", "popdensity", "arrivals", "departures", "vul_emp", "GNI", "health_exp", "pollution", "EIU_Democracy", "trust", "risktaking")
label.list <- c("Proportion 65+", "Proportion Urban", "Latitude", "Longitude", "Population Density", "Log(Arrivals)", "Log(Departures)", "Vulnerable Employees", "Log(GNI per capita)", "Health Expenditures", "Pollution", "EIU Democracy", "Trust", "Risk Taking")

p.dat.list <- list()
s.list <- list()

for (i in c(1:length(var.list))){

   var <- var.list[i]
   label <- label.list[i]
   
   if (var == "arrivals" || var == "departures" || var == "GNI"){
         regmoda<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14))+Lag(StringencyIndex,c(14)):","log(",var,")")),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
   regmodb<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21))+Lag(StringencyIndex,c(21)):","log(",var,")")),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
   regmodc<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28))+Lag(StringencyIndex,c(28)):","log(",var,")")),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
   regmodd<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35))+Lag(StringencyIndex,c(35)):","log(",var,")")),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
   }else{
   regmoda<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14))+Lag(StringencyIndex,c(14)):",var)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
   regmodb<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21))+Lag(StringencyIndex,c(21)):",var)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
   regmodc<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28))+Lag(StringencyIndex,c(28)):",var)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
   regmodd<-plm(as.formula(paste("Total_Mortality_Rate_Growth~Lag(Total_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35))+Lag(StringencyIndex,c(35)):",var)),method="pooling",effect="twoways",data=p.dat_cum[which(!is.infinite(p.dat_cum$Total_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
   }
   
   stargazer(digits=6,regmoda,regmodb,regmodc,regmodd,type="html",se=list(se.a[,2],se.b[,2],se.c[,2],se.d[,2]),out=paste("Table_Appendix_panel_",var,"_weekly_reg_output.htm"),
          dep.var.labels=c("Cumulative Mortality Growth (t)"),
          dep.var.labels.include = FALSE,
          covariate.labels=c("Cum. Mortality Growth (t-7)","Stringency (t-14)",paste("Stringency (t-14) X ", label),"Cum. Mortality Growth (t-14)","Stringency (t-21)",paste("Stringency (t-21) X ", label),"Cum. Mortality Growth (t-21)","Stringency (t-28)",paste("Stringency (t-28) X ", label),"Cum. Mortality Growth (t-28)","Stringency (t-35)",paste("Stringency (t-35) X ", label)), df = FALSE,omit.stat="adj.rsq",notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","HAC robust standard errors, clustered by country. Time and Country FEs."),notes.append=F,notes.align ="l",title=paste("Heterogeneity: ", label),add.lines = list(c("Fixed effects?", "Y", "Y","Y","Y")))
   
    ### plot of 10-unit increase in Stringency effects over time on future mortality growth
   p.dat<-c(coef(regmoda)[2],coef(regmodb)[2],coef(regmodc)[2],coef(regmodd)[2])
   i.dat<-c(coef(regmoda)[3],coef(regmodb)[3],coef(regmodc)[3],coef(regmodd)[3])
   effect.dat<-cbind(p.dat, i.dat)
   #p.dat<-cbind(p.dat,p.dat+1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
   #p.dat<-cbind(p.dat,p.dat[,1]-1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
   colnames(effect.dat)<-c("mean.1", "mean.2")
   
   p.dat.list[[i]]<-data.frame(effect.dat)*10*100
   
   ###figure out the 25% percentile  and 75% percentile of elderly population rate across countries, to compare effects of a 10-unit rise in stringency level
   if (var == "arrivals" || var == "departures" || var == "GNI"){
      
   s.list[[i]] <- summary(unique(log(p.dat_cum[[var]])))
   }else{
      s.list[[i]] <- summary(unique(p.dat_cum[[var]]))
   }
   s.list
}

## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Proportion 65+</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.075232</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.052933)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.002952</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002433)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Proportion 65+</td><td>-0.000741<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000147)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>-0.002603</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.034343)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.000323</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001853)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Proportion 65+</td><td></td><td>-0.000476<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000106)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.059215<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026212)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.002296</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001457)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Proportion 65+</td><td></td><td></td><td>-0.000262<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000120)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.084322<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.035553)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003998<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.002198)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Proportion 65+</td><td></td><td></td><td></td><td>-0.000029</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000148)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.183163</td><td>0.197537</td><td>0.178810</td><td>0.124033</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>126.916600<sup>***</sup></td><td>109.542500<sup>***</sup></td><td>70.258980<sup>***</sup></td><td>29.498940<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Proportion Urban</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.103173<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.056754)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.003729</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.004706)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Proportion Urban</td><td>-0.000150<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000059)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.014070</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.037639)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.000371</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003759)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Proportion Urban</td><td></td><td>-0.000100<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000048)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.047184<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026737)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.003409</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002788)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Proportion Urban</td><td></td><td></td><td>-0.000035</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000041)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.083614<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.033956)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003924</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003092)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Proportion Urban</td><td></td><td></td><td></td><td>-0.000007</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000047)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.130300</td><td>0.155460</td><td>0.155396</td><td>0.123904</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>84.798780<sup>***</sup></td><td>81.914130<sup>***</sup></td><td>59.366550<sup>***</sup></td><td>29.464150<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Latitude</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.091215</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.057141)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.001517</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002388)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Latitude</td><td>-0.000150<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000040)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.015918</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.036961)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.004441<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002217)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Latitude</td><td></td><td>-0.000062<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000032)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.050375<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027041)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.004255<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001608)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Latitude</td><td></td><td></td><td>-0.000045<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000020)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.085421<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.033133)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003774<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001434)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Latitude</td><td></td><td></td><td></td><td>-0.000018</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000030)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.154164</td><td>0.149144</td><td>0.163187</td><td>0.125262</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>103.160300<sup>***</sup></td><td>78.002870<sup>***</sup></td><td>62.923420<sup>***</sup></td><td>29.833310<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Longitude</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.107097<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.056050)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.006999<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002008)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Longitude</td><td>0.000044<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000019)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.015748</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.038032)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.006766<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002038)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Longitude</td><td></td><td>0.000037<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000018)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.045166<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026575)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.005919<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001736)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Longitude</td><td></td><td></td><td>0.000011</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000015)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.086320<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.032725)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.004601<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001479)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Longitude</td><td></td><td></td><td></td><td>0.000015</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000020)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.131542</td><td>0.170127</td><td>0.155553</td><td>0.130370</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>85.729790<sup>***</sup></td><td>91.226900<sup>***</sup></td><td>59.437240<sup>***</sup></td><td>31.232150<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Population Density</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.126328<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.058592)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.006724<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001857)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Population Density</td><td>0.0000004</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000001)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.030691</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.036516)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007053<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002024)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Population Density</td><td></td><td>0.000003</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000004)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.041399</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026243)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006096<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001894)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Population Density</td><td></td><td></td><td>0.000001</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000003)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.080699<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.028253)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.005161<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001833)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Population Density</td><td></td><td></td><td></td><td>0.000004</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000005)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.104353</td><td>0.135275</td><td>0.151898</td><td>0.126755</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>65.945510<sup>***</sup></td><td>69.614570<sup>***</sup></td><td>57.790700<sup>***</sup></td><td>30.240540<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(Arrivals)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.104480<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.055939)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.044519<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.014335)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(Arrivals)</td><td>-0.003076<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000855)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.011777</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.032059)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.044774<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.011129)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(Arrivals)</td><td></td><td>-0.003087<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000691)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.052692<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.022080)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.032350<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.011842)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(Arrivals)</td><td></td><td></td><td>-0.002296<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000759)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.092806<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.030015)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.029739<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.012662)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(Arrivals)</td><td></td><td></td><td></td><td>-0.002048<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000803)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.143237</td><td>0.210514</td><td>0.210993</td><td>0.168633</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>94.626010<sup>***</sup></td><td>118.657500<sup>***</sup></td><td>86.286100<sup>***</sup></td><td>42.257950<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(Departures)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.119221<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.056501)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.034250<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.016669)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(Departures)</td><td>-0.002578<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000990)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.012672</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.030975)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.026553<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.014353)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(Departures)</td><td></td><td>-0.002158<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000895)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.081513<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.030486)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.028357<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.010418)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(Departures)</td><td></td><td></td><td>-0.002189<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000678)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.092880<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.044006)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.036899<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.010839)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(Departures)</td><td></td><td></td><td></td><td>-0.002535<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000719)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,604</td><td>1,291</td><td>968</td><td>650</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.194975</td><td>0.315715</td><td>0.369098</td><td>0.245081</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>118.838300<sup>***</sup></td><td>179.322800<sup>***</sup></td><td>165.759000<sup>***</sup></td><td>58.652730<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Vulnerable Employees</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.105264<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.056265)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.009835<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001965)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Vulnerable Employees</td><td>0.000158<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000071)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.022316</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.037793)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007647<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002168)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Vulnerable Employees</td><td></td><td>0.000053</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000052)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.044589<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026810)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006279<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002116)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Vulnerable Employees</td><td></td><td></td><td>0.000020</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000046)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.085713<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.033703)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.004941<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001942)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Vulnerable Employees</td><td></td><td></td><td></td><td>0.000028</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000053)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.129922</td><td>0.138502</td><td>0.152607</td><td>0.125520</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>84.516550<sup>***</sup></td><td>71.542310<sup>***</sup></td><td>58.108830<sup>***</sup></td><td>29.903570<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(GNI per capita)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.097395<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.054196)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.022969<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.008963)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(GNI per capita)</td><td>-0.003051<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000895)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.011759</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.036610)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.011792<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.007114)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(GNI per capita)</td><td></td><td>-0.001890<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000737)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.049609<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.026893)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.002661</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.005209)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(GNI per capita)</td><td></td><td></td><td>-0.000874</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000591)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.084918<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.033721)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.001720</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.006171)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(GNI per capita)</td><td></td><td></td><td></td><td>-0.000273</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000668)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.153794</td><td>0.170634</td><td>0.162819</td><td>0.124692</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>102.867700<sup>***</sup></td><td>91.554300<sup>***</sup></td><td>62.753710<sup>***</sup></td><td>29.677990<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Health Expenditures</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.108591<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.058168)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.003926<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001869)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Health Expenditures</td><td>-0.000001<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.0000003)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.017615</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.039096)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.004372<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001754)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Health Expenditures</td><td></td><td>-0.000001<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.0000003)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.047640<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027409)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.004588<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001495)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Health Expenditures</td><td></td><td></td><td>-0.0000005</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.0000003)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.084502<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.031001)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003898<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001282)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Health Expenditures</td><td></td><td></td><td></td><td>-0.0000002</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.0000002)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.143209</td><td>0.180522</td><td>0.171206</td><td>0.126027</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>94.604600<sup>***</sup></td><td>98.028330<sup>***</sup></td><td>66.653960<sup>***</sup></td><td>30.041820<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Pollution</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.121538<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.058210)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.008450<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001891)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Pollution</td><td>0.000069<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000019)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.025266</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.037170)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.008395<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002009)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Pollution</td><td></td><td>0.000070<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000030)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.043744</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027262)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006751<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001949)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Pollution</td><td></td><td></td><td>0.000034</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000023)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.083370<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.030041)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.005010<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001621)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Pollution</td><td></td><td></td><td></td><td>0.000026</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000017)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.114877</td><td>0.154403</td><td>0.158611</td><td>0.126791</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>73.458810<sup>***</sup></td><td>81.255530<sup>***</sup></td><td>60.826100<sup>***</sup></td><td>30.250230<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: EIU Democracy</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.118904<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.057705)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.000664</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.003637)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X EIU Democracy</td><td>-0.001073<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000443)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.022984</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.037570)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.001000</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002591)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X EIU Democracy</td><td></td><td>-0.001099<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000345)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.045614<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027451)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.002008</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001914)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X EIU Democracy</td><td></td><td></td><td>-0.000551<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000300)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.083701<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.031253)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.003151</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.002307)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X EIU Democracy</td><td></td><td></td><td></td><td>-0.000174</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000354)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,838</td><td>1,468</td><td>1,092</td><td>737</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.119085</td><td>0.163146</td><td>0.161532</td><td>0.124466</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>76.513970<sup>***</sup></td><td>86.753590<sup>***</sup></td><td>62.162280<sup>***</sup></td><td>29.616560<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Trust</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.212853<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.053297)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.007698<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001974)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Trust</td><td>0.009153<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.003998)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.047107</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.044812)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007957<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002014)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Trust</td><td></td><td>0.006320<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003475)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.045732</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.030809)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006647<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001880)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Trust</td><td></td><td></td><td>0.000370</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003403)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.096406<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.032801)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.004803<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001715)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Trust</td><td></td><td></td><td></td><td>-0.002180</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004019)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,482</td><td>1,208</td><td>924</td><td>643</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.194906</td><td>0.179490</td><td>0.171133</td><td>0.145638</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>109.425000<sup>***</sup></td><td>79.407650<sup>***</sup></td><td>55.883380<sup>***</sup></td><td>30.740290<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Risk Taking</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Growth (t-7)</td><td>0.213216<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.053427)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.006621<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001802)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Risk Taking</td><td>0.001681</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007420)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-14)</td><td></td><td>0.053208</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.044486)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007507<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002027)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Risk Taking</td><td></td><td>-0.003379</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.005674)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-21)</td><td></td><td></td><td>-0.043984</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.032246)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006671<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001800)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Risk Taking</td><td></td><td></td><td>-0.001016</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003943)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Cum. Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.087099<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.034356)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>-0.005275<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001531)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Risk Taking</td><td></td><td></td><td></td><td>-0.005940</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004463)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,482</td><td>1,208</td><td>924</td><td>643</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.168322</td><td>0.159572</td><td>0.171362</td><td>0.152460</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>91.479720<sup>***</sup></td><td>68.922730<sup>***</sup></td><td>55.973940<sup>***</sup></td><td>32.439340<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>

Plot Proportion 65+

   #plot
   Plot_34 <- ggplot(data=p.dat.list[[1]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*19.400))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*19.400),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+geom_point(size=4,aes(y=mean.1+mean.2*8.283),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*8.283)),linetype=2)+ggtitle(label.list[1])+ylim(-20,10)
   Plot_34

Plot Proportion Urban

   #plot
   Plot_35 <- ggplot(data=p.dat.list[[2]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*86.56))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*86.56),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*66.47),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*66.47)),linetype=2)+ggtitle(label.list[2])+ylim(-20,10)
   Plot_35

Plot Latitude

   #plot
   Plot_36 <- ggplot(data=p.dat.list[[3]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*50.83))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*50.83),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*22.01),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*22.01)),linetype=2)+ggtitle(label.list[3])+ylim(-20,10)
   Plot_36

Plot Longitude

   #plot
   Plot_37 <- ggplot(data=p.dat.list[[4]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*49.333))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*49.333),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*3.342),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*3.342)),linetype=2)+ggtitle(label.list[4])+ylim(-20,10)
   Plot_37

Plot Population Density

   #plot
   Plot_38 <- ggplot(data=p.dat.list[[5]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*223.847))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*223.847),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*33.375),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*33.375)),linetype=2)+ggtitle(label.list[5])+ylim(-20,10)
   Plot_38

Plot Log Arrivals

   #plot
   Plot_39 <- ggplot(data=p.dat.list[[6]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.94))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.94),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*15.30),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*15.30)),linetype=2)+ggtitle(label.list[6])+ylim(-20,10)
   Plot_39

Plot Log Departures

   #plot
   Plot_40 <- ggplot(data=p.dat.list[[7]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.79))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.79),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*15.02),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*15.02)),linetype=2)+ggtitle(label.list[7])+ylim(-20,10)
   Plot_40

Plot Vulnerable Employees

   #plot
   Plot_41 <- ggplot(data=p.dat.list[[8]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*21.797))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*21.797),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*7.407),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*7.407)),linetype=2)+ggtitle(label.list[8])+ylim(-20,10)
   Plot_41

Plot Log(GNI per capita)

   Plot_42 <- ggplot(data=p.dat.list[[9]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*10.747))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*10.747),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*9.242),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*9.242)),linetype=2)+ggtitle(label.list[9])+ylim(-20,10)
   Plot_42

Plot Health Expenditures

   Plot_43 <- ggplot(data=p.dat.list[[10]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*4292.73))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*4292.73),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*542.55),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*542.55)),linetype=2)+ggtitle(label.list[10])+ylim(-20,10)
   Plot_43

Plot Pollution

   Plot_44 <- ggplot(data=p.dat.list[[11]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*23.084))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*23.084),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*10.419),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*10.419)),linetype=2)+ggtitle(label.list[11])+ylim(-20,10)
   Plot_44

Plot EIU Democracy

   Plot_45 <- ggplot(data=p.dat.list[[12]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*8.207))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*8.207),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*6.570),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*6.570)),linetype=2)+ggtitle(label.list[12])+ylim(-20,10)
   Plot_45

Plot Trust

   Plot_46 <- ggplot(data=p.dat.list[[13]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.28083))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.28083),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*-0.13294),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*-0.13294)),linetype=2)+ggtitle(label.list[13])+ylim(-20,10)
   Plot_46

Plot Risk Taking

   Plot_47 <- ggplot(data=p.dat.list[[14]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.06918))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.06918),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on Cumulative Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*-0.15774),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*-0.15774)),linetype=2)+ggtitle(label.list[14])+ylim(-20,10)
   Plot_47

Arrange all plots

Plot_48 <- grid.arrange(Plot_34, Plot_35, Plot_36, Plot_37, Plot_38, Plot_39, Plot_40, Plot_41, Plot_42, Plot_43, Plot_44, Plot_45, ncol=4,nrow=3)

Plot_48

## TableGrob (3 x 4) "arrange": 12 grobs
##     z     cells    name           grob
## 1   1 (1-1,1-1) arrange gtable[layout]
## 2   2 (1-1,2-2) arrange gtable[layout]
## 3   3 (1-1,3-3) arrange gtable[layout]
## 4   4 (1-1,4-4) arrange gtable[layout]
## 5   5 (2-2,1-1) arrange gtable[layout]
## 6   6 (2-2,2-2) arrange gtable[layout]
## 7   7 (2-2,3-3) arrange gtable[layout]
## 8   8 (2-2,4-4) arrange gtable[layout]
## 9   9 (3-3,1-1) arrange gtable[layout]
## 10 10 (3-3,2-2) arrange gtable[layout]
## 11 11 (3-3,3-3) arrange gtable[layout]
## 12 12 (3-3,4-4) arrange gtable[layout]

Weining Cross-Sectional Analysis - Cumulative Mortality

library(lubridate)
library(zoo)
library(quantmod)
library(fBasics)
library(tseries)
library(sandwich)
library(lmtest)
library(lattice)
library(xtable)
library(vars)
library(plyr)
library(gridExtra)
library(corrplot)
library(ggplot2)
library(reshape2)
library(data.table)
library(rvest)
library(plm)
library(stringr)
library(stargazer)
library(survival)
library(ggfortify)

## Warning: package 'ggfortify' was built under R version 3.6.2

library(haven)

Merging Global Preferences Survey data.

# Remove factor variables for government responses
dat_cs <-Final_Data_Country[,-c(48:125,151:154)]

gps.country <- read_dta("data/global_preference_survey_country.dta")
# Change country names for merge
gps.country[which(gps.country$country == "South Korea"),"country"] <- "Korea, South"
gps.country[which(gps.country$country == "Czech Republic"),"country"] <- "Czechia"
gps.country[which(gps.country$country == "United States"),"country"] <- "US"

gps.individual <- read_dta("data/global_preference_survey_individual.dta")
# Change country names for merge
gps.individual[which(gps.individual$country == "South Korea"),"country"] <- "Korea, South"
gps.individual[which(gps.individual$country == "Czech Republic"),"country"] <- "Czechia"
gps.individual[which(gps.individual$country == "United States"),"country"] <- "US"

gps.individual$age <- as.character(gps.individual$age)
gps.individual[which(gps.individual$age == "99 99+"), "age"] <- "99"
gps.individual[which(gps.individual$age == "100 (Refused"), "age"] <- NA
gps.individual$age <- as.numeric(gps.individual$age)
gps.individual.pop65 <- gps.individual[which(!is.na(gps.individual$age) & gps.individual$age > 65), ]
gps.individual.pop65 <- gps.individual.pop65[which(!is.na(gps.individual.pop65$trust)),]
gps.country.pop65 <- ddply(gps.individual.pop65, "country", function(X) data.frame(wm.trust = weighted.mean(X$trust, X$wgt)))

colnames(gps.country)[1] <- "COUNTRY"
dat_cs <- merge(dat_cs, gps.country, by=c("COUNTRY"), all.x=TRUE)
colnames(gps.country.pop65)[1] <- "COUNTRY"
dat_cs <- merge(dat_cs, gps.country.pop65, by=c("COUNTRY"), all.x=TRUE)

Renaming variables and creating some new variables.

cs_cumulative<-pdata.frame(dat_cs, index = c("COUNTRY", "Date"))
cs_cumulative$Date<-as.Date(cs_cumulative$Date,"%Y-%m-%d")
#reorder
cs_cumulative<-cs_cumulative[order(cs_cumulative$Date),]
cs_cumulative<-cs_cumulative[order(cs_cumulative$COUNTRY),]

colnames(cs_cumulative)[4] <- "Total_Case_Country"
colnames(cs_cumulative)[5] <- "Total_Death_Country"
colnames(cs_cumulative)[6] <- "New_Case_Country"
colnames(cs_cumulative)[7] <- "New_Death_Country"
colnames(cs_cumulative)[9] <- "Total_Mortality_Rate"
colnames(cs_cumulative)[10] <- "New_Mortality_Rate"
colnames(cs_cumulative)[11] <- "Total_Case_Rate"
colnames(cs_cumulative)[12] <- "RollingAverage_New_Case_Country"
colnames(cs_cumulative)[13] <- "RollingAverage_New_Death_Country"
colnames(cs_cumulative)[14] <- "RollingAverage_Total_Mortality_Rate"
colnames(cs_cumulative)[15] <- "RollingAverage_New_Mortality_Rate"
colnames(cs_cumulative)[16] <- "EIU_Democracy"

cs_cumulative$New_Case_Rate = cs_cumulative$New_Case_Country/cs_cumulative$Population

cs_cumulative <- cs_cumulative %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(RollingAverage_Total_Case_Country = frollmean(Total_Case_Country, 7, na.rm=TRUE))
cs_cumulative <- cs_cumulative %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(RollingAverage_Total_Death_Country = frollmean(Total_Death_Country, 7,na.rm=TRUE))

cs_cumulative <- cs_cumulative %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(RollingAverage_Total_Case_Rate = frollmean(Total_Case_Rate, 7,na.rm=TRUE))

cs_cumulative <- cs_cumulative %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(RollingAverage_New_Case_Rate = frollmean(New_Case_Rate, 7,na.rm=TRUE))

cs_cumulative <- cs_cumulative %>%
  dplyr::select(colnames(cs_cumulative)[c(1:8,11,81,9,10,82,12,83,13,84,85,14,15)], everything())

cs_cumulative <- cs_cumulative %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(Total_Mortality_Rate_Growth = log(RollingAverage_Total_Mortality_Rate) - log(lag(RollingAverage_Total_Mortality_Rate,7)))

cs_cumulative$respiratory_deaths_rate <- 1/3*(cs_cumulative$respiratory_deaths/cs_cumulative$Population)

colnames(cs_cumulative)

##  [1] "COUNTRY"                              
##  [2] "X1"                                   
##  [3] "Date"                                 
##  [4] "Total_Case_Country"                   
##  [5] "Total_Death_Country"                  
##  [6] "New_Case_Country"                     
##  [7] "New_Death_Country"                    
##  [8] "Population"                           
##  [9] "Total_Case_Rate"                      
## [10] "risktaking"                           
## [11] "Total_Mortality_Rate"                 
## [12] "New_Mortality_Rate"                   
## [13] "posrecip"                             
## [14] "RollingAverage_New_Case_Country"      
## [15] "negrecip"                             
## [16] "RollingAverage_New_Death_Country"     
## [17] "altruism"                             
## [18] "trust"                                
## [19] "RollingAverage_Total_Mortality_Rate"  
## [20] "RollingAverage_New_Mortality_Rate"    
## [21] "EIU_Democracy"                        
## [22] "longitude"                            
## [23] "C1_School.closing"                    
## [24] "C1_Flag"                              
## [25] "C2_Workplace.closing"                 
## [26] "C2_Flag"                              
## [27] "C3_Cancel.public.events"              
## [28] "C3_Flag"                              
## [29] "C4_Restrictions.on.gatherings"        
## [30] "C4_Flag"                              
## [31] "C5_Close.public.transport"            
## [32] "C5_Flag"                              
## [33] "C6_Stay.at.home.requirements"         
## [34] "C6_Flag"                              
## [35] "C7_Restrictions.on.internal.movement" 
## [36] "C7_Flag"                              
## [37] "C8_International.travel.controls"     
## [38] "E1_Income.support"                    
## [39] "E1_Flag"                              
## [40] "E2_Debt.contract.relief"              
## [41] "E3_Fiscal.measures"                   
## [42] "H1_Public.information.campaigns"      
## [43] "H1_Flag"                              
## [44] "H2_Testing.policy"                    
## [45] "H3_Contact.tracing"                   
## [46] "H4_Emergency.investment.in.healthcare"
## [47] "H5_Investment.in.vaccines"            
## [48] "M1_Wildcard"                          
## [49] "StringencyIndex"                      
## [50] "LegacyStringencyIndex"                
## [51] "Oxford_Cases"                         
## [52] "Oxford_Deaths"                        
## [53] "Latitude"                             
## [54] "Longitude"                            
## [55] "prop65"                               
## [56] "propurban"                            
## [57] "popdensity"                           
## [58] "driving"                              
## [59] "walking"                              
## [60] "transit"                              
## [61] "arrivals"                             
## [62] "departures"                           
## [63] "vul_emp"                              
## [64] "GNI"                                  
## [65] "health_exp"                           
## [66] "pollution"                            
## [67] "EUI_democracy"                        
## [68] "freedom_house"                        
## [69] "cellular_sub"                         
## [70] "milex"                                
## [71] "milper"                               
## [72] "irst"                                 
## [73] "pec"                                  
## [74] "cinc"                                 
## [75] "endocrine_deaths"                     
## [76] "blood_pressure_deaths"                
## [77] "respiratory_deaths"                   
## [78] "govt_accountability"                  
## [79] "political_stability"                  
## [80] "govt_effectiveness"                   
## [81] "regulatory_quality"                   
## [82] "rule_of_law"                          
## [83] "control_of_corruption"                
## [84] "isocode"                              
## [85] "patience"                             
## [86] "wm.trust"                             
## [87] "New_Case_Rate"                        
## [88] "RollingAverage_Total_Case_Country"    
## [89] "RollingAverage_Total_Death_Country"   
## [90] "RollingAverage_Total_Case_Rate"       
## [91] "RollingAverage_New_Case_Rate"         
## [92] "Total_Mortality_Rate_Growth"          
## [93] "respiratory_deaths_rate"

Generating key endogenous variables used in cross-country analysis.

first.date.mortality<-c(0)
first.date.case<-c(0)

peak.date.mortality<-c(0)
peak.date.case<-c(0)

days.to.peak.mortality<-c(0)
days.to.peak.case<-c(0)
peak.or.no.mortality<-c(0)
peak.or.no.case<-c(0)
country <- c("")

peak.mortality<-c(0)
peak.case<-c(0)

early.mortality<-c(0)
early.case<-c(0)

peak.new.mortality<-c(0)
peak.new.case<-c(0)

early.stringency<-c(0)
peak.stringency<-c(0)
peak.date.stringency<-c(0)

early.mobility.walking<-c(0)
early.mortality.growth<-c(0)
early.case.growth<-c(0)

dems_data<-aggregate(data=cs_cumulative,cbind(prop65,propurban,popdensity,vul_emp,health_exp,GNI,pollution,Latitude,Longitude,EIU_Democracy,freedom_house)~COUNTRY,FUN=mean,na.rm=T)

for(i in 1:length(unique(dems_data$COUNTRY))){
  
  bb<-subset(cs_cumulative,COUNTRY==unique(dems_data$COUNTRY)[i])
  country[i] <- as.character(unique(dems_data$COUNTRY)[i])
  bb<-bb[order(bb$Date),]
  bb<-bb[order(bb$COUNTRY),]
  bb <- bb %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(RollingAverage_Walking = frollmean(walking, 7))
  bb <- bb %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(RollingAverage_StringencyIndex = frollmean(StringencyIndex, 7))
  
  peak.new.mortality[i]<-max(bb$RollingAverage_New_Mortality_Rate,na.rm = TRUE) # Peak new mortality
  peak.new.case[i]<-max(bb$RollingAverage_New_Case_Rate, na.rm = TRUE) # Peak new case
  
  peak.date.mortality[i]<-as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate==peak.new.mortality[i]))]) # Peak date of new mortality
  first.date.mortality[i]<-as.character(bb$Date[which(bb$Total_Death_Country>0)[1]]) # Date of first death
  peak.mortality[i]<-bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate==peak.new.mortality[i])] # Total mortality rate at the peak

  peak.date.case[i]<-as.character(bb$Date[which((bb$RollingAverage_New_Case_Rate==peak.new.case[i]))]) # Peak date of new case
  first.date.case[i]<-as.character(bb$Date[which(bb$Total_Case_Country>0)[1]]) # Date of first case
  peak.case[i]<-bb$RollingAverage_Total_Case_Rate[which.max(bb$RollingAverage_New_Case_Rate==peak.new.case[i])] # Toal case rate at the peak
  
  days.to.peak.mortality[i]<-as.Date(peak.date.mortality[i])-as.Date(first.date.mortality[i])
  peak.or.no.mortality[i]<-ifelse(peak.date.mortality[i]==max(bb$Date),0,1)
  
  days.to.peak.case[i]<-as.Date(peak.date.case[i])-as.Date(first.date.case[i])
  peak.or.no.case[i]<-ifelse(peak.date.case[i]==max(bb$Date),0,1)
  
  early.mortality[i]<-bb$RollingAverage_Total_Mortality_Rate[which(bb$Total_Death_Country>0)[7]] # Total death rate in the first week since first death
  early.mortality.growth[i]<-log(bb$RollingAverage_New_Mortality_Rate[which(bb$Total_Death_Country>0)[7]]) -  log(bb$RollingAverage_New_Mortality_Rate[which(bb$Total_Death_Country>0)[1]])# Growth rate of new mortality rate in the first week since first death
  
  early.case[i]<-bb$RollingAverage_Total_Case_Rate[which(bb$Total_Case_Country>0)[7]] # Total case rate in the first week since first case
  if (bb$RollingAverage_New_Case_Rate[which(bb$Total_Case_Country>0)[1]] == 0){
    early.case.growth[i]<-log(bb$RollingAverage_New_Case_Rate[which(bb$Total_Case_Country>0)[7]]) -  log(bb$RollingAverage_Total_Case_Rate[which(bb$Total_Case_Country>0)[1]])# Growth rate of new case rate in the first week since first case
  }else{
    early.case.growth[i]<-log(bb$RollingAverage_New_Case_Rate[which(bb$Total_Case_Country>0)[7]]) -  log(bb$RollingAverage_New_Case_Rate[which(bb$Total_Case_Country>0)[1]])# Growth rate of new case rate in the first week since first case
  }
  early.mobility.walking[i]<-bb$RollingAverage_Walking[which(bb$Total_Death_Country>0)[1]-1] # Weekly average walking mobility in the week prior to first death
  
  early.stringency[i]<-bb$RollingAverage_StringencyIndex[which(bb$Total_Death_Country>0)[1]-7] #Weekly average stringency index in the week prior to first death
  peak.stringency[i]<-max(bb$StringencyIndex,na.rm=T)
  
  if (peak.stringency[i] == -Inf){
    peak.date.stringency[i] <- -Inf
  }else{
    peak.date.stringency[i]<-as.character(bb$Date[which.max(bb$StringencyIndex)])
  }
}

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in max(bb$StringencyIndex, na.rm = T): no non-missing arguments to max;
## returning -Inf

## Warning in max(bb$StringencyIndex, na.rm = T): no non-missing arguments to max;
## returning -Inf

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in max(bb$StringencyIndex, na.rm = T): no non-missing arguments to max;
## returning -Inf

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.mortality[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Mortality_Rate == : number of
## items to replace is not a multiple of replacement length

## Warning in peak.mortality[i] <-
## bb$RollingAverage_Total_Mortality_Rate[which(bb$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.date.case[i] <-
## as.character(bb$Date[which((bb$RollingAverage_New_Case_Rate == : number of items
## to replace is not a multiple of replacement length

stringency.growth.to.max<-(peak.stringency-early.stringency)/as.numeric(as.Date(peak.date.stringency)-as.Date(first.date.mortality))
log.peak.mortality.to.duration <- log(peak.new.mortality)/as.numeric(days.to.peak.mortality)
log.peak.case.to.duration <- log(peak.new.case)/as.numeric(days.to.peak.case)

# set NA early stringency to 0
# early.string[is.na(early.string)]<-0

Cumulative Mortality Cross-Sectional Dataset.

surv.dat<-data.frame(country,days.to.peak.mortality,peak.or.no.mortality,
                     days.to.peak.case,peak.or.no.case,
                     early.mortality,early.mortality.growth,
                     early.case,early.case.growth,
                     peak.mortality,peak.case,peak.new.mortality,peak.new.case,
                     log.peak.mortality.to.duration,log.peak.case.to.duration,
                     early.stringency,stringency.growth.to.max,peak.stringency,early.mobility.walking,dems_data[,-1])

#remove vietnam
surv.dat<-surv.dat[-nrow(surv.dat),]
surv.dat<-mutate(surv.dat,stringent=ifelse(early.stringency>19,"SI>19","SI<19"),stringent=factor(stringent))

Plotting K-P curves for confirmed cases.

km_fit <- survfit(Surv(days.to.peak.case,peak.or.no.case) ~1, data=surv.dat)

p1<-autoplot(km_fit)+theme_bw()+xlab("Number of Days")+ylab("Probability Case Peak is Yet to Come")
km_fit2<-survfit(Surv(days.to.peak.case,peak.or.no.case)~stringent, data=surv.dat)
p2<-autoplot(km_fit2)+theme_bw()+xlab("Number of Days")+ylab("Probability Case Peak is Yet to Come")+theme(legend.position = c(0.2, 0.2),legend.title=element_blank())
p3<-ggplot(data=surv.dat,aes(x=early.stringency,fill=stringent))+geom_histogram(color="black",alpha=.5)+theme_bw()+xlab("Average SI in the Week Prior to First Death")+ylab("Frequency of Countries")+theme(legend.title = element_blank(),legend.position = c(0.8,0.6))
g <- grid.arrange(p1, p2, p3, nrow=1)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 3 rows containing non-finite values (stat_bin).

# g <- arrangeGrob(p1,p2,p3,nrow=1)
ggsave("Figure_cox_case_appendix.png", g, width = 15, height = 7)

Plotting K-P curves for cumulative mortality.

km_fit <- survfit(Surv(days.to.peak.mortality,peak.or.no.mortality) ~1, data=surv.dat)

p1<-autoplot(km_fit)+theme_bw()+xlab("Number of Days")+ylab("Probability Mortality Peak is Yet to Come")
km_fit2<-survfit(Surv(days.to.peak.mortality,peak.or.no.mortality)~stringent, data=surv.dat)
p2<-autoplot(km_fit2)+theme_bw()+xlab("Number of Days")+ylab("Probability Mortality Peak is Yet to Come")+theme(legend.position = c(0.2, 0.2),legend.title=element_blank())
p3<-ggplot(data=surv.dat,aes(x=early.stringency,fill=stringent))+geom_histogram(color="black",alpha=.5)+theme_bw()+xlab("Average SI in the Week Prior to First Death")+ylab("Frequency of Countries")+theme(legend.title = element_blank(),legend.position = c(0.8,0.6))
g <- grid.arrange(p1, p2, p3, nrow=1)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 3 rows containing non-finite values (stat_bin).

# g <- arrangeGrob(p1,p2,p3,nrow=1)
ggsave("Figure_cox_mortality_appendix.png", g, width = 15, height = 7)

Cox proportional hazard regression.

cox.death<-coxph(Surv(days.to.peak.mortality,peak.or.no.mortality) ~
                   log(peak.new.mortality)+log(early.mortality)+early.mortality.growth+
                   early.stringency+stringency.growth.to.max+early.mobility.walking+
                   prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)

cox.case<-coxph(Surv(days.to.peak.case,peak.or.no.case) ~
                   log(peak.new.case)+log(early.case)+early.case.growth+
                   early.stringency+stringency.growth.to.max+early.mobility.walking+
                   prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)

Testing the proportional hazards assumption.

test.death<-cox.zph(cox.death)$table[nrow(cox.zph(cox.death)$table),ncol(cox.zph(cox.death)$table)]
test.case<-cox.zph(cox.case)$table[nrow(cox.zph(cox.case)$table),ncol(cox.zph(cox.case)$table)]

HTML output of the Cox hazard model

stargazer(cox.death,cox.case,type="html",out="Table_cox_reg_output_appendix.htm",
          dep.var.labels=c("Survival Probability of Mortality Peaking at Time (t)", "Survival Probability of Case Peaking at Time (t)"), 
          covariate.labels = c("Log(Peak Mortality)", "Log(Early Mortality)", "Early Mortality Growth",
                               "Log(Peak Case)", "Log(Early Case)", "Early Case Growth",
                              "Early Stringency","Stringency Growth to Maximum", "Early Mobility",
                              "Prop. 65+","Prop. Urban","Pop. Density","Vulnerable Emp.",
                              "Log(GNI)","EIU Democracy","Latitude-Longitude"),
          df = FALSE,omit.stat =c("max.rsq","logrank"),
          notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","PH Test refers to testing the proportional hazards assumption (Grambsch and Therneau (1994)).","Null hypothesis is the assumption is not violated."),
          notes.append=F,notes.align ="l",
          title=("Cox Proportional Hazards Regression: Duration to Peak Mortality (Appendix)"),
          add.lines = list(c("PH Test p-value",round(test.death,3),round(test.case,3))))

Cross-country analysis of peak mortality.

### - peak cum. mortality - 
cs.total.mortality<- lm(log(peak.mortality) ~ 
                          log(early.mortality)+early.mortality.growth+
                          early.stringency+stringency.growth.to.max+early.mobility.walking+
                          prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)
se.total.mortality<-coeftest(cs.total.mortality, vcov = vcovHC(cs.total.mortality, type = "HC1"))

### - peak new mortality - 
cs.new.mortality<- lm(log(peak.new.mortality) ~ 
                       log(early.mortality)+early.mortality.growth+
                       early.stringency+stringency.growth.to.max+early.mobility.walking+
                       prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)
se.new.mortality<-coeftest(cs.new.mortality, vcov = vcovHC(cs.new.mortality, type = "HC1"))

### - peak new mortality to duration ratio - 
cs.peak.duration.ratio<- lm(log.peak.mortality.to.duration ~ 
                                  log(early.mortality)+early.mortality.growth+
                                  early.stringency+stringency.growth.to.max+early.mobility.walking+
                                  prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat)
se.peak.duration.ratio<-coeftest(cs.peak.duration.ratio, vcov = vcovHC(cs.peak.duration.ratio, type = "HC1"))

HTML output of the cross-country analysis

stargazer(cs.total.mortality,cs.new.mortality,cs.peak.duration.ratio,
          se=list(se.total.mortality[,2],se.new.mortality[,2],se.peak.duration.ratio[,2]),type="html",out="Table_cs_reg_output_appendix.htm",intercept.bottom = FALSE,
          dep.var.labels=c("Log(Peak Cum. Mortality Rate)", "Log(Peak New Mortality Rate)", "Peak New Moratlity Rate to Duration Rato"),
          covariate.labels=c("Intercept","Log(Early Mortality)","Early Mortality Growth",
                             "Early Stringency","Stringency Growth to Maximum", "Early Mobility",
                             "Prop. 65+","Prop. Urban","Pop. Density","Vulnerable Emp.", "Log(GNI)",
                             "EIU Democracy","Latitude-Longitude"), 
          df = FALSE,notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","Heteroscedastic-Robust standard errors"),
          notes.append=F,notes.align ="l",font.size = "tiny",
          title=("Cross-Country Regression: Explaining Differences in First-Wave Mortality Rates (Appendix)"))

Table of variable definitions and sources

df.definition <- data.frame("Variable" = c("Cum. Mortality Rate", "New Mortality Rate",
                                           "Early Mortality", "Early Mortality Growth", "Peak Cum. Mortality", "Peak New Mortality",
                                           "PD to Peak Mortality", "Logged Peak Mortality-to-PD",
                                           "Early Confirmed Case", "Early Confirmed Case Growth", "Peak Cum. Confirmed Case", "Peak New Confirmed Case",
                                           "PD to Peak Confirmed Case", "Logged Peak Case-to-PD", 
                                           "Early Stringency", "Stringency Delta", "Peak Stringency",
                                           "Early Mobility",
                                           "Prop. 65+", "Prop. Urban", "Pop. Density", 
                                           "Vulnerable Employment","Health Expenditure","Log(GNI)","Pollution",
                                           "Tourist Arrivals", "Tourist Departures",
                                           "Latitude","Longitude","Democracy"),
                            "Definition" = c("7-day rolling average of cumulative mortality rate out of the total population",
                                             "7-day rolling average of daily new mortality rate out ot the total population",
                                             "Cumulative mortality rate in the week following the first death",
                                             "Growth rate of new mortality rate in the week following the first death",
                                             "Cumulative mortality rate at the peak of new mortality rate in the first quasi-bell curve",
                                             "New mortality rate at the peak of new mortality rate in the first quasi-bell curve",
                                             "Day-to-peak of new mortality rate in the first quasi-bell curve",
                                             "The ratio of the logged peak new mortality rate to the PD to peak mortality",
                                             "Cumulative confirmed case rate in the week following the first case",
                                             "Growth rate of new confirmed case rate in the week following the first case",
                                             "Cumulative confirmed case rate at the peak of new confirmed case rate in the first quasi-bell curve",
                                             "New confirmed case rate at the peak of new confirmed case rate in the first quasi-bell curve",
                                             "Day-to-peak of new confirmed case rate in the first quasi-bell curve",
                                             "The ratio of the logged peak new confirmed case rate to the PD to peak confirmed case",
                                             "Weekly average stringency index in the week prior to the first death",
                                             "Growth rate of SI from the week prior to the first death to the maximum level of stringency",
                                             "Maximum level of stringency index in the first quasi-bell curve",
                                             "Weekly average level of mobility in terms of walking in the week prior to the first death, reported by Apple",
                                             "Elderly population (people aged 65 and over) as a percentage of the total population",
                                             "Urban population as a percentage of the total population",
                                             "Midyear population divided by land area in square kilometers",
                                             "Employment in vulnerable sectors (i.e., family workers and own-account workers) as a percentage of the total employment",
                                             "Level of current health expenditure (including healthcare goods and services) as a percentage of GDP",
                                             "The logged gross national income per capita",
                                             "Population-weighted exposure to ambient PM2.5 pollution",
                                             "International inbound tourists to the country",
                                             "International outbound tourists from the country",
                                             "Latitude coordinate of the country",
                                             "Longitude coordinate of the country",
                                             "The Democracy index calculated by The Economist Intelligence Unit"
                                             ),
                            "Source" = c("Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on JHU COVID-19 Data",
                                         "Authors' calculation based on OxCGRT Data",
                                         "Authors' calculation based on OxCGRT Data",
                                         "Authors' calculation based on OxCGRT Data",
                                         "Authors' calculation based on Apple COVID-19 Mobility Trends Reports",
                                         "World Development Indicators",
                                         "World Development Indicators",
                                         "World Development Indicators",
                                         "World Development Indicators",
                                         "World Development Indicators",
                                         "World Development Indicators",
                                         "World Development Indicators",
                                         "World Development Indicators",
                                         "World Development Indicators",
                                         "Country-level coordinates from Google",
                                         "Country-level coordinates from Google",
                                         "The EIU Democracy Index 2019 Database"
                                         )
                              )
rownames(df.definition) <- NULL
stargazer(df.definition,type="html",rownames = FALSE, summary = FALSE, out="Table_variable_definition.htm")

## 
## <table style="text-align:center"><tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Variable</td><td>Definition</td><td>Source</td></tr>
## <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Cum. Mortality Rate</td><td>7-day rolling average of cumulative mortality rate out of the total population</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">New Mortality Rate</td><td>7-day rolling average of daily new mortality rate out ot the total population</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Mortality</td><td>Cumulative mortality rate in the week following the first death</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Mortality Growth</td><td>Growth rate of new mortality rate in the week following the first death</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Peak Cum. Mortality</td><td>Cumulative mortality rate at the peak of new mortality rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Peak New Mortality</td><td>New mortality rate at the peak of new mortality rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">PD to Peak Mortality</td><td>Day-to-peak of new mortality rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Logged Peak Mortality-to-PD</td><td>The ratio of the logged peak new mortality rate to the PD to peak mortality</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Confirmed Case</td><td>Cumulative confirmed case rate in the week following the first case</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Confirmed Case Growth</td><td>Growth rate of new confirmed case rate in the week following the first case</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Peak Cum. Confirmed Case</td><td>Cumulative confirmed case rate at the peak of new confirmed case rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Peak New Confirmed Case</td><td>New confirmed case rate at the peak of new confirmed case rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">PD to Peak Confirmed Case</td><td>Day-to-peak of new confirmed case rate in the first quasi-bell curve</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Logged Peak Case-to-PD</td><td>The ratio of the logged peak new confirmed case rate to the PD to peak confirmed case</td><td>Authors' calculation based on JHU COVID-19 Data</td></tr>
## <tr><td style="text-align:left">Early Stringency</td><td>Weekly average stringency index in the week prior to the first death</td><td>Authors' calculation based on OxCGRT Data</td></tr>
## <tr><td style="text-align:left">Stringency Delta</td><td>Growth rate of SI from the week prior to the first death to the maximum level of stringency</td><td>Authors' calculation based on OxCGRT Data</td></tr>
## <tr><td style="text-align:left">Peak Stringency</td><td>Maximum level of stringency index in the first quasi-bell curve</td><td>Authors' calculation based on OxCGRT Data</td></tr>
## <tr><td style="text-align:left">Early Mobility</td><td>Weekly average level of mobility in terms of walking in the week prior to the first death, reported by Apple</td><td>Authors' calculation based on Apple COVID-19 Mobility Trends Reports</td></tr>
## <tr><td style="text-align:left">Prop. 65+</td><td>Elderly population (people aged 65 and over) as a percentage of the total population</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Prop. Urban</td><td>Urban population as a percentage of the total population</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Pop. Density</td><td>Midyear population divided by land area in square kilometers</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Vulnerable Employment</td><td>Employment in vulnerable sectors (i.e., family workers and own-account workers) as a percentage of the total employment</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Health Expenditure</td><td>Level of current health expenditure (including healthcare goods and services) as a percentage of GDP</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Log(GNI)</td><td>The logged gross national income per capita</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Pollution</td><td>Population-weighted exposure to ambient PM2.5 pollution</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Tourist Arrivals</td><td>International inbound tourists to the country</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Tourist Departures</td><td>International outbound tourists from the country</td><td>World Development Indicators</td></tr>
## <tr><td style="text-align:left">Latitude</td><td>Latitude coordinate of the country</td><td>Country-level coordinates from Google</td></tr>
## <tr><td style="text-align:left">Longitude</td><td>Longitude coordinate of the country</td><td>Country-level coordinates from Google</td></tr>
## <tr><td style="text-align:left">Democracy</td><td>The Democracy index calculated by The Economist Intelligence Unit</td><td>The EIU Democracy Index 2019 Database</td></tr>
## <tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr></table>

WEEKLY MORTALITY RATES - Panel + Cross-Sectional Analysis

PANEL REGRESSION

### Creating new weekly mortality growth and running libraries.
library(lubridate)
library(zoo)
library(quantmod)
library(fBasics)
library(tseries)
library(sandwich)
library(lmtest)
library(lattice)
library(xtable)
library(vars)
library(plyr)
library(dplyr)
library(gridExtra)
library(corrplot)
library(ggplot2)
library(reshape2)
library(data.table)
library(rvest)
library(plm)
library(stringr)
library(tidyverse)
library(foreign)
library(stargazer)
library(haven)
# Remove factor variables for government responses
datweekly_panel<-Final_Data_Country
datweekly_panel<-datweekly_panel %>% dplyr::select(-contains("Flag"))
datweekly_panel<-datweekly_panel %>% dplyr::select(-contains("Lagged"))
datweekly_panel$latitude <- NULL
datweekly_panel$longitude <- NULL

Merging global preference survey data.

gps.country <- read_dta("data/global_preference_survey_country.dta")

# Change country names for merge
gps.country[which(gps.country$country == "South Korea"),"country"] <- "Korea, South"
gps.country[which(gps.country$country == "Czech Republic"),"country"] <- "Czechia"
gps.country[which(gps.country$country == "United States"),"country"] <- "US"

gps.individual <- read_dta("data/global_preference_survey_individual.dta")

# Change country names for merge
gps.individual[which(gps.individual$country == "South Korea"),"country"] <- "Korea, South"
gps.individual[which(gps.individual$country == "Czech Republic"),"country"] <- "Czechia"
gps.individual[which(gps.individual$country == "United States"),"country"] <- "US"

gps.individual$age <- as.character(gps.individual$age)
gps.individual[which(gps.individual$age == "99 99+"), "age"] <- "99"
gps.individual[which(gps.individual$age == "100 (Refused"), "age"] <- NA
gps.individual$age <- as.numeric(gps.individual$age)
gps.individual.pop65 <- gps.individual[which(!is.na(gps.individual$age) & gps.individual$age > 65), ]
gps.individual.pop65 <- gps.individual.pop65[which(!is.na(gps.individual.pop65$trust)),]
gps.country.pop65 <- ddply(gps.individual.pop65, "country", function(X) data.frame(wm.trust = weighted.mean(X$trust, X$wgt)))

colnames(gps.country)[1] <- "COUNTRY"
datweekly_panel <- merge(datweekly_panel, gps.country, by=c("COUNTRY"), all.x=TRUE)
colnames(gps.country.pop65)[1] <- "COUNTRY"
datweekly_panel <- merge(datweekly_panel, gps.country.pop65, by=c("COUNTRY"), all.x=TRUE)

Renaming variables and creating some new variables.

datweekly_panel<-pdata.frame(datweekly_panel, index = c("COUNTRY", "Date"))
datweekly_panel$Date<-as.Date(datweekly_panel$Date,"%Y-%m-%d")
#reorder
datweekly_panel<-datweekly_panel[order(datweekly_panel$Date),]
datweekly_panel<-datweekly_panel[order(datweekly_panel$COUNTRY),]

colnames(datweekly_panel)[colnames(datweekly_panel) == "Total_Cases_Country"] <- "Total_Case_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "Total_Deceased_Country"] <- "Total_Death_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "New_Confirmed_Country"] <- "New_Case_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "New_Total_Deceased_Country"] <- "New_Death_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "Total_Mortality_Rate_Per_Capita"] <- "Total_Mortality_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "New_Mortality_Rate_Per_Capita"] <- "New_Mortality_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "Total_Cases_Country_Per_Capita"] <- "Total_Case_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "rolling_average_confirmed"] <- "RollingAverage_New_Case_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "rolling_average_deceased"] <- "RollingAverage_New_Death_Country"
colnames(datweekly_panel)[colnames(datweekly_panel) == "total_rolling_average_mortality"] <- "RollingAverage_Total_Mortality_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "new_rolling_average_mortality"] <- "RollingAverage_New_Mortality_Rate"
colnames(datweekly_panel)[colnames(datweekly_panel) == "EUI_democracy"] <- "EIU_Democracy"

datweekly_panel$RollingAverage_Total_Mortality_Rate <- NULL
datweekly_panel$longitude <- NULL
datweekly_panel$latitude <- NULL

datweekly_panel$New_Case_Rate = datweekly_panel$New_Case_Country/datweekly_panel$Population

datweekly_panel <- datweekly_panel %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(RollingAverage_New_Case_Rate = frollmean(New_Case_Rate, 7,na.rm=TRUE))

datweekly_panel <- datweekly_panel %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(Total_Mortality_Rate_Growth = log(Total_Mortality_Rate) - Lag(log(Total_Mortality_Rate),7))

datweekly_panel <- datweekly_panel %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(Total_Case_Rate_Growth = log(Total_Case_Rate) - Lag(log(Total_Case_Rate),7))

datweekly_panel <- datweekly_panel %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(New_Case_Rate_Growth = log(RollingAverage_New_Case_Rate) - Lag(log(RollingAverage_New_Case_Rate),7))

datweekly_panel <- datweekly_panel %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(New_Mortality_Rate_Growth = log(RollingAverage_New_Mortality_Rate) - Lag(log(RollingAverage_New_Mortality_Rate),7))

datweekly_panel$respiratory_deaths_rate <- 1/3*(datweekly_panel$respiratory_deaths/datweekly_panel$Population)

### Reorder columns
datweekly_panel <- datweekly_panel %>%
  dplyr::select(c("COUNTRY", "X1", "Date", "Total_Case_Country", "Total_Death_Country", "New_Case_Country", "New_Death_Country", "Population", "Total_Case_Rate", "Total_Mortality_Rate", "New_Case_Rate", "New_Mortality_Rate", "RollingAverage_New_Case_Country", "RollingAverage_New_Death_Country", "RollingAverage_New_Case_Rate", "RollingAverage_New_Mortality_Rate", "Total_Case_Rate_Growth", "Total_Mortality_Rate_Growth", "New_Case_Rate_Growth", "New_Mortality_Rate_Growth"), everything())

datweekly_panel$Total_Case_Rate_Growth[is.nan(datweekly_panel$Total_Case_Rate_Growth)] <- NA
datweekly_panel$Total_Mortality_Rate_Growth[is.nan(datweekly_panel$Total_Mortality_Rate_Growth)] <- NA

datweekly_panel$New_Case_Rate_Growth[is.nan(datweekly_panel$New_Case_Rate_Growth)] <- NA
datweekly_panel$New_Mortality_Rate_Growth[is.nan(datweekly_panel$New_Mortality_Rate_Growth)] <- NA

datweekly_panel$New_Mortality_Rate_Growth[is.infinite(-datweekly_panel$New_Mortality_Rate_Growth)] <- Inf

colnames(datweekly_panel)

##  [1] "COUNTRY"                              
##  [2] "X1"                                   
##  [3] "Date"                                 
##  [4] "Total_Case_Country"                   
##  [5] "Total_Death_Country"                  
##  [6] "New_Case_Country"                     
##  [7] "New_Death_Country"                    
##  [8] "Population"                           
##  [9] "Total_Case_Rate"                      
## [10] "Total_Mortality_Rate"                 
## [11] "New_Case_Rate"                        
## [12] "New_Mortality_Rate"                   
## [13] "RollingAverage_New_Case_Country"      
## [14] "RollingAverage_New_Death_Country"     
## [15] "RollingAverage_New_Case_Rate"         
## [16] "RollingAverage_New_Mortality_Rate"    
## [17] "Total_Case_Rate_Growth"               
## [18] "Total_Mortality_Rate_Growth"          
## [19] "New_Case_Rate_Growth"                 
## [20] "New_Mortality_Rate_Growth"            
## [21] "C1_School.closing"                    
## [22] "C2_Workplace.closing"                 
## [23] "C3_Cancel.public.events"              
## [24] "C4_Restrictions.on.gatherings"        
## [25] "C5_Close.public.transport"            
## [26] "C6_Stay.at.home.requirements"         
## [27] "C7_Restrictions.on.internal.movement" 
## [28] "C8_International.travel.controls"     
## [29] "E1_Income.support"                    
## [30] "E2_Debt.contract.relief"              
## [31] "E3_Fiscal.measures"                   
## [32] "H1_Public.information.campaigns"      
## [33] "H2_Testing.policy"                    
## [34] "H3_Contact.tracing"                   
## [35] "H4_Emergency.investment.in.healthcare"
## [36] "H5_Investment.in.vaccines"            
## [37] "M1_Wildcard"                          
## [38] "StringencyIndex"                      
## [39] "LegacyStringencyIndex"                
## [40] "Oxford_Cases"                         
## [41] "Oxford_Deaths"                        
## [42] "Latitude"                             
## [43] "Longitude"                            
## [44] "prop65"                               
## [45] "propurban"                            
## [46] "popdensity"                           
## [47] "driving"                              
## [48] "walking"                              
## [49] "transit"                              
## [50] "arrivals"                             
## [51] "departures"                           
## [52] "vul_emp"                              
## [53] "GNI"                                  
## [54] "health_exp"                           
## [55] "pollution"                            
## [56] "EIU_Democracy"                        
## [57] "freedom_house"                        
## [58] "cellular_sub"                         
## [59] "milex"                                
## [60] "milper"                               
## [61] "irst"                                 
## [62] "pec"                                  
## [63] "cinc"                                 
## [64] "endocrine_deaths"                     
## [65] "blood_pressure_deaths"                
## [66] "respiratory_deaths"                   
## [67] "govt_accountability"                  
## [68] "political_stability"                  
## [69] "govt_effectiveness"                   
## [70] "regulatory_quality"                   
## [71] "rule_of_law"                          
## [72] "control_of_corruption"                
## [73] "isocode"                              
## [74] "patience"                             
## [75] "risktaking"                           
## [76] "posrecip"                             
## [77] "negrecip"                             
## [78] "altruism"                             
## [79] "trust"                                
## [80] "wm.trust"                             
## [81] "respiratory_deaths_rate"

Baseline regressions

datweekly_panel <- datweekly_panel %>% dplyr::ungroup()
datweekly_panel<-pdata.frame(datweekly_panel, index = c("COUNTRY", "Date"))
datweekly_panel<-datweekly_panel[order(datweekly_panel$Date),]
datweekly_panel<-datweekly_panel[order(datweekly_panel$COUNTRY),]

###baseline projection regressions, remove 'infinite' values. two-way FE, HAC robust SEs, clustered on country
regmoda<-plm(New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))

regmodb<-plm(New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))

regmodc<-plm(New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))

regmodd<-plm(New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))

HTML output of baseline regression results

stargazer(digits=6,regmoda,regmodb,regmodc,regmodd,type="html",se=list(se.a[,2],se.b[,2],se.c[,2],se.d[,2]), out="Table_panel_reg_output_weekly.htm",
          dep.var.labels=c("Weekly Avg. New Mortality Growth (t)"),
          covariate.labels=c("New Mortality Growth (t-7)","Stringency (t-14)","New Mortality Growth (t-14)","Stringency (t-21)","New Mortality Growth (t-21)","Stringency (t-28)","New Mortality Growth (t-28)","Stringency (t-35)"), df = FALSE,omit.stat="adj.rsq",notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","HAC robust standard errors, clustered by country. Time and Country FEs."),notes.append=F,notes.align ="l",title="Table 1. Mortality Projection - Average Impact",add.lines = list(c("Fixed effects?", "Y", "Y","Y","Y")))

Plots

### plot of 10-unit increase in Stringency effects over time on future mortality growth
p.dat_weekly<-c(coef(regmoda)[2],coef(regmodb)[2],coef(regmodc)[2],coef(regmodd)[2])
p.dat_weekly<-cbind(p.dat_weekly,p.dat_weekly+1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
p.dat_weekly<-cbind(p.dat_weekly,p.dat_weekly[,1]-1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
colnames(p.dat_weekly)<-c("mean","upper","lower")
p.dat_weekly<-data.frame(p.dat_weekly)*10*100

Plot_59 <-ggplot(data=p.dat_weekly,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean))+geom_point(size=4,shape=21,fill="grey")+geom_hline(yintercept=0,linetype=2)+xlab("Increase in Stringency Index of 10 units")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+geom_errorbar(aes(ymin=lower,ymax=upper))
Plot_59

Generating Figure 5.

Plot_60 <-ggplot(data=p.dat_weekly,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean))+geom_point(size=4,shape=21,fill="grey")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+geom_errorbar(aes(ymin=lower,ymax=upper))+ggtitle("Average Impact")
Plot_60

Loop to generate heterogeneity regression results

###baseline projection regressions, remove 'infinite' values. two-way FE, HAC robust SEs, clustered on country
var.list <- c("prop65", "propurban", "Latitude", "Longitude", "popdensity", "arrivals", "departures", "vul_emp", "GNI", "health_exp", "pollution", "EIU_Democracy", "trust", "risktaking")
label.list <- c("Proportion 65+", "Proportion Urban", "Latitude", "Longitude", "Population Density", "Log(Arrivals)", "Log(Departures)", "Vulnerable Employees", "Log(GNI per capita)", "Health Expenditures", "Pollution", "EIU Democracy", "Trust", "Risk Taking")

p.dat_weekly.list <- list()
s.list <- list()

for (i in c(1:length(var.list))){

   var <- var.list[i]
   label <- label.list[i]
   
   if (var == "arrivals" || var == "departures" || var == "GNI"){
         regmoda<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14))+Lag(StringencyIndex,c(14)):","log(",var,")")),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
   regmodb<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21))+Lag(StringencyIndex,c(21)):","log(",var,")")),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
   regmodc<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28))+Lag(StringencyIndex,c(28)):","log(",var,")")),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
   regmodd<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35))+Lag(StringencyIndex,c(35)):","log(",var,")")),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
   }else{
   regmoda<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,7)+Lag(StringencyIndex,c(14))+Lag(StringencyIndex,c(14)):",var)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.a<-coeftest(regmoda, vcov = vcovHC(regmoda, type = "HC1", cluster="group"))
   regmodb<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,14)+Lag(StringencyIndex,c(21))+Lag(StringencyIndex,c(21)):",var)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.b<-coeftest(regmodb, vcov = vcovHC(regmodb, type = "HC1", cluster="group"))
   regmodc<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,21)+Lag(StringencyIndex,c(28))+Lag(StringencyIndex,c(28)):",var)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.c<-coeftest(regmodc, vcov = vcovHC(regmodc, type = "HC1", cluster="group"))
   regmodd<-plm(as.formula(paste("New_Mortality_Rate_Growth~Lag(New_Mortality_Rate_Growth,28)+Lag(StringencyIndex,c(35))+Lag(StringencyIndex,c(35)):",var)),method="pooling",effect="twoways",data=datweekly_panel[which(!is.infinite(datweekly_panel$New_Mortality_Rate_Growth)),],na.action="na.exclude")
   se.d<-coeftest(regmodd, vcov = vcovHC(regmodd, type = "HC1", cluster="group"))
   }
   
   stargazer(digits=6,regmoda,regmodb,regmodc,regmodd,type="html",
             se=list(se.a[,2],se.b[,2],se.c[,2],se.d[,2]),out=paste("Table_panel_",var,"_weekly_reg_output.htm"),
             dep.var.labels=c("Weekly Avg. New Mortality Growth (t)"),
             dep.var.labels.include = FALSE,
             covariate.labels=c("New Mortality Growth (t-7)","Stringency (t-14)",paste("Stringency (t-14) X ", label),"New Mortality Growth (t-14)","Stringency (t-21)", paste("Stringency (t-21) X ", label),"New Mortality Growth (t-21)","Stringency (t-28)",paste("Stringency (t-28) X ", label),"New Mortality Growth (t-28)","Stringency (t-35)",paste("Stringency (t-35) X ", label)), 
             df = FALSE,omit.stat="adj.rsq",
             notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.","HAC robust standard errors, clustered by country. Time and Country FEs."),notes.append=F,notes.align ="l",
             title=paste("Heterogeneity: ", label),add.lines = list(c("Fixed effects?", "Y", "Y","Y","Y")))
   
   ### plot of 10-unit increase in Stringency effects over time on future mortality growth
   p.dat<-c(coef(regmoda)[2],coef(regmodb)[2],coef(regmodc)[2],coef(regmodd)[2])
   i.dat<-c(coef(regmoda)[3],coef(regmodb)[3],coef(regmodc)[3],coef(regmodd)[3])
   effect.dat<-cbind(p.dat, i.dat)
   #p.dat_weekly<-cbind(p.dat_weekly,p.dat_weekly+1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
   #p.dat_weekly<-cbind(p.dat_weekly,p.dat_weekly[,1]-1.96*c(se.a[2,2],se.b[2,2],se.c[2,2],se.d[2,2]))
   colnames(effect.dat)<-c("mean.1", "mean.2")
   
   p.dat_weekly.list[[i]]<-data.frame(effect.dat)*10*100
   
   ###figure out the 25% percentile  and 75% percentile of elderly population rate across countries, to compare effects of a 10-unit rise in stringency level
   if (var == "arrivals" || var == "departures" || var == "GNI"){
      
   s.list[[i]] <- summary(unique(log(datweekly_panel[[var]])))
   }else{
      s.list[[i]] <- summary(unique(datweekly_panel[[var]]))
   }
   s.list
}

## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Proportion 65+</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.261511<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.121918)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.000745</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.004765)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Proportion 65+</td><td>-0.001028<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000303)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.040736</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.048724)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.003301</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003939)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Proportion 65+</td><td></td><td>-0.000570<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000220)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.029495</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.042448)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.005481<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003018)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Proportion 65+</td><td></td><td></td><td>-0.000203</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000272)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.015473</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.043583)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.009380</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.010026)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Proportion 65+</td><td></td><td></td><td></td><td>-0.000338</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000432)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.150457</td><td>0.064602</td><td>0.026340</td><td>0.005453</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>92.330240<sup>***</sup></td><td>27.763570<sup>***</sup></td><td>7.737080<sup>***</sup></td><td>0.953975</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Proportion Urban</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.236219<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.119189)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.006118</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007232)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Proportion Urban</td><td>-0.000118</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000088)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.054976</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.051941)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.003739</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.006496)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Proportion Urban</td><td></td><td>-0.000104</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000082)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.023535</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.037377)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.007939<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.004088)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Proportion Urban</td><td></td><td></td><td>-0.000006</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000057)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.006323</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041461)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.004777</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.008539)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Proportion Urban</td><td></td><td></td><td></td><td>-0.000013</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000089)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.120837</td><td>0.056109</td><td>0.025050</td><td>0.003403</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>71.655010<sup>***</sup></td><td>23.896850<sup>***</sup></td><td>7.348300<sup>***</sup></td><td>0.594178</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Latitude</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.246198<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.118344)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.009319<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005551)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Latitude</td><td>-0.000148<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000043)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.053647</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.052712)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.009554<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003773)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Latitude</td><td></td><td>-0.000042</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000046)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.036790</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.041330)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.004511<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002604)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Latitude</td><td></td><td></td><td>-0.000105<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000038)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.018244</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.043100)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.007855</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004937)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Latitude</td><td></td><td></td><td></td><td>-0.000101</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000079)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.129371</td><td>0.054092</td><td>0.030796</td><td>0.006625</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>77.467780<sup>***</sup></td><td>22.988500<sup>***</sup></td><td>9.087527<sup>***</sup></td><td>1.160423</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Longitude</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.231688<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.118265)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.014507<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006002)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Longitude</td><td>0.000009</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000032)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.059887</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.054177)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.010953<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003307)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Longitude</td><td></td><td>0.000040</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000031)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.022741</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.036893)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.008332<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003102)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Longitude</td><td></td><td></td><td>0.000014</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000033)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.000573</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.043903)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.003713</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003585)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Longitude</td><td></td><td></td><td></td><td>0.000055<sup>*</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000030)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.117473</td><td>0.058134</td><td>0.025541</td><td>0.007858</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>69.394320<sup>***</sup></td><td>24.812490<sup>***</sup></td><td>7.496293<sup>***</sup></td><td>1.378190</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Population Density</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.231754<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.118832)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.014459<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006120)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Population Density</td><td>-0.0000002</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000001)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.059289</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.052100)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.011071<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003273)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Population Density</td><td></td><td>-0.0000001</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000004)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.024046</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.039558)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.008202<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003314)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Population Density</td><td></td><td></td><td>-0.000001</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000012)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.008760</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.039992)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.004394</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004921)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Population Density</td><td></td><td></td><td></td><td>-0.000003</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000010)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.117253</td><td>0.053016</td><td>0.025076</td><td>0.003531</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>69.247660<sup>***</sup></td><td>22.505740<sup>***</sup></td><td>7.356068<sup>***</sup></td><td>0.616531</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(Arrivals)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.263589<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.124604)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.079898<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.031679)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(Arrivals)</td><td>-0.005681<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002100)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.041271</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.050360)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.038956<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.017781)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(Arrivals)</td><td></td><td>-0.003006<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001097)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.036370</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.042291)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.035451</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.031132)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(Arrivals)</td><td></td><td></td><td>-0.002627</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001925)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.028886</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.046779)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.080781</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.051125)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(Arrivals)</td><td></td><td></td><td></td><td>-0.004594</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.002868)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.147066</td><td>0.062341</td><td>0.031470</td><td>0.017067</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>89.890560<sup>***</sup></td><td>26.727330<sup>***</sup></td><td>9.292778<sup>***</sup></td><td>3.021141<sup>**</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(Departures)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.231778</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.144257)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.054784</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.035236)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(Departures)</td><td>-0.004288<sup>*</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.002356)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.060583</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.051637)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.026756</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.023222)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(Departures)</td><td></td><td>-0.002408<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001411)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.035649</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.042712)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.047318<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.027988)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(Departures)</td><td></td><td></td><td>-0.003410<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.001761)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.026784</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.040929)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.113854<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.055900)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(Departures)</td><td></td><td></td><td></td><td>-0.006691<sup>**</sup></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003203)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,514</td><td>1,202</td><td>893</td><td>583</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.134003</td><td>0.072635</td><td>0.034779</td><td>0.024029</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>71.282650<sup>***</sup></td><td>28.170470<sup>***</sup></td><td>9.332314<sup>***</sup></td><td>3.906484<sup>***</sup></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Vulnerable Employees</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.234258<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.117743)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.015837<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006286)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Vulnerable Employees</td><td>0.000070</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000099)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.054748</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.051335)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.012609<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003519)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Vulnerable Employees</td><td></td><td>0.000080</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000074)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.027050</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.039010)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.009775<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003842)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Vulnerable Employees</td><td></td><td></td><td>0.000073</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000078)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.011169</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041045)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.001574</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003695)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Vulnerable Employees</td><td></td><td></td><td></td><td>0.000139</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000099)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.118373</td><td>0.054715</td><td>0.026329</td><td>0.005970</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>69.997600<sup>***</sup></td><td>23.268420<sup>***</sup></td><td>7.733820<sup>***</sup></td><td>1.044999</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Log(GNI per capita)</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.242076<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.119478)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>0.017041</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.011580)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Log(GNI per capita)</td><td>-0.003216<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001298)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.050261</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.050354)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.012260</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.012375)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Log(GNI per capita)</td><td></td><td>-0.002377<sup>*</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.001250)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.026257</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.039385)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>0.001174</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.008215)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Log(GNI per capita)</td><td></td><td></td><td>-0.000970</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000998)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.009403</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041592)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.016801</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.018615)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Log(GNI per capita)</td><td></td><td></td><td></td><td>-0.001279</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.001635)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.129493</td><td>0.060706</td><td>0.026220</td><td>0.004594</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>77.551230<sup>***</sup></td><td>25.981200<sup>***</sup></td><td>7.700693<sup>***</sup></td><td>0.802989</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Health Expenditures</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.237573<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.120195)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.010912<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.005384)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Health Expenditures</td><td>-0.000001<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000001)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.056802</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.052433)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.007974<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002921)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Health Expenditures</td><td></td><td>-0.000001<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000001)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.023449</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.038346)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.006223<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002380)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Health Expenditures</td><td></td><td></td><td>-0.000001</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.0000005)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.005785</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.039591)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.005761</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.004756)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Health Expenditures</td><td></td><td></td><td></td><td>-0.000001</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.0000005)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.131083</td><td>0.063997</td><td>0.029219</td><td>0.004849</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>78.647570<sup>***</sup></td><td>27.485730<sup>***</sup></td><td>8.608131<sup>***</sup></td><td>0.847922</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Pollution</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.237309<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.117813)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.018075<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006254)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Pollution</td><td>0.000134<sup>***</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000029)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.054267</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.050796)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.013989<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003481)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Pollution</td><td></td><td>0.000110<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000049)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.024210</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.037703)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.009278<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003563)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Pollution</td><td></td><td></td><td>0.000034</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000032)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.006080</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041129)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.003341</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003683)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Pollution</td><td></td><td></td><td></td><td>0.000022</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000042)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.127752</td><td>0.061051</td><td>0.025746</td><td>0.003526</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>76.355910<sup>***</sup></td><td>26.138350<sup>***</sup></td><td>7.557832<sup>***</sup></td><td>0.615741</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: EIU Democracy</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.235646<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.116939)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.002949</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.007507)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X EIU Democracy</td><td>-0.001693<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.000706)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.056518</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.051092)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>0.000692</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.004095)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X EIU Democracy</td><td></td><td>-0.001699<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.000672)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.024135</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.038203)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.004520</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.002899)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X EIU Democracy</td><td></td><td></td><td>-0.000545</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.000575)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.005643</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.041240)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.002610</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.006496)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X EIU Democracy</td><td></td><td></td><td></td><td>0.000158</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.000712)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,704</td><td>1,336</td><td>980</td><td>633</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.126636</td><td>0.063481</td><td>0.025955</td><td>0.003415</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>75.592020<sup>***</sup></td><td>27.249300<sup>***</sup></td><td>7.620978<sup>***</sup></td><td>0.596332</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Trust</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.207884</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.167395)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.016783<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006789)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Trust</td><td>0.003630</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.008616)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.097951</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.066224)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.012771<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.003266)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Trust</td><td></td><td>0.005042</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.007544)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.071893<sup>*</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.039017)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.008255<sup>**</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003425)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Trust</td><td></td><td></td><td>-0.005469</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.006660)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>-0.004932</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.055082)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.003610</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003581)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Trust</td><td></td><td></td><td></td><td>-0.002255</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.007371)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,376</td><td>1,104</td><td>827</td><td>551</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.119749</td><td>0.068103</td><td>0.028151</td><td>0.002569</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>56.682980<sup>***</sup></td><td>24.018810<sup>***</sup></td><td>6.913423<sup>***</sup></td><td>0.385438</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>
## 
## <table style="text-align:center"><caption><strong>Heterogeneity: Risk Taking</strong></caption>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="4"><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="4" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">New Mortality Growth (t-7)</td><td>-0.210581</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.164528)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14)</td><td>-0.017332<sup>**</sup></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.006990)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-14) X Risk Taking</td><td>-0.010745</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td>(0.009817)</td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-14)</td><td></td><td>0.097186</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.061682)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21)</td><td></td><td>-0.013928<sup>***</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.002994)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-21) X Risk Taking</td><td></td><td>-0.019579<sup>**</sup></td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td>(0.007787)</td><td></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-21)</td><td></td><td></td><td>-0.055504</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.041683)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28)</td><td></td><td></td><td>-0.009309<sup>***</sup></td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.003510)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-28) X Risk Taking</td><td></td><td></td><td>-0.007615</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td>(0.008346)</td><td></td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">New Mortality Growth (t-28)</td><td></td><td></td><td></td><td>0.007168</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.050674)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35)</td><td></td><td></td><td></td><td>0.002730</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.003350)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td style="text-align:left">Stringency (t-35) X Risk Taking</td><td></td><td></td><td></td><td>-0.010828</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td>(0.007906)</td></tr>
## <tr><td style="text-align:left"></td><td></td><td></td><td></td><td></td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Fixed effects?</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr>
## <tr><td style="text-align:left">Observations</td><td>1,376</td><td>1,104</td><td>827</td><td>551</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.122537</td><td>0.078939</td><td>0.027853</td><td>0.004253</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>58.187130<sup>***</sup></td><td>28.168080<sup>***</sup></td><td>6.838061<sup>***</sup></td><td>0.639211</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td colspan="4" style="text-align:left">*,**,*** correspond to 10%, 5% and 1% significance, respectively.</td></tr>
## <tr><td style="text-align:left"></td><td colspan="4" style="text-align:left">HAC robust standard errors, clustered by country. Time and Country FEs.</td></tr>
## </table>

Plot Proportion 65+

   #plot
   Plot_61 <- ggplot(data=p.dat_weekly.list[[1]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*19.400))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*19.400),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+geom_point(size=4,aes(y=mean.1+mean.2*8.283),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*8.283)),linetype=2)+ggtitle(label.list[1])+ylim(-25,15)
   Plot_61

Plot Proportion Urban

   #plot
   Plot_62 <- ggplot(data=p.dat_weekly.list[[2]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*86.56))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*86.56),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*66.47),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*66.47)),linetype=2)+ggtitle(label.list[2])+ylim(-25,15)
   Plot_62

Plot Latitude

   #plot
   Plot_63 <- ggplot(data=p.dat_weekly.list[[3]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*50.83))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*50.83),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*22.01),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*22.01)),linetype=2)+ggtitle(label.list[3])+ylim(-25,15)
   Plot_63

Plot Longitude

   #plot
   Plot_64 <- ggplot(data=p.dat_weekly.list[[4]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*49.333))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*49.333),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*3.342),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*3.342)),linetype=2)+ggtitle(label.list[4])+ylim(-25,15)
   Plot_64

Plot Population Density

   #plot
   Plot_65 <- ggplot(data=p.dat_weekly.list[[5]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*223.847))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*223.847),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*33.375),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*33.375)),linetype=2)+ggtitle(label.list[5])+ylim(-25,15)
   Plot_65

Plot Log Arrivals

   #plot
   Plot_66 <- ggplot(data=p.dat_weekly.list[[6]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.94))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.94),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*15.30),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*15.30)),linetype=2)+ggtitle(label.list[6])+ylim(-25,15)
   Plot_66

Plot Log Departures

   #plot
   Plot_68 <- ggplot(data=p.dat_weekly.list[[7]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.79))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*16.79),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*15.02),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*15.02)),linetype=2)+ggtitle(label.list[7])+ylim(-25,15)
   Plot_68

Plot Vulnerable Employees

   #plot
   Plot_69 <- ggplot(data=p.dat_weekly.list[[8]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*21.797))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*21.797),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*7.407),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*7.407)),linetype=2)+ggtitle(label.list[8])+ylim(-25,15)
   Plot_69

Plot Log(GNI per capita)

   Plot_70 <- ggplot(data=p.dat_weekly.list[[9]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*10.747))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*10.747),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*9.242),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*9.242)),linetype=2)+ggtitle(label.list[9])+ylim(-25,15)
   Plot_70

Plot Health Expenditures

   Plot_71 <- ggplot(data=p.dat_weekly.list[[10]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*4292.73))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*4292.73),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*542.55),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*542.55)),linetype=2)+ggtitle(label.list[10])+ylim(-25,15)
   Plot_71

Plot Pollution

   Plot_72 <- ggplot(data=p.dat_weekly.list[[11]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*23.084))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*23.084),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*10.419),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*10.419)),linetype=2)+ggtitle(label.list[11])+ylim(-25,15)
   Plot_72

Plot EIU Democracy

   Plot_73 <- ggplot(data=p.dat_weekly.list[[12]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*8.207))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*8.207),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*6.570),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*6.570)),linetype=2)+ggtitle(label.list[12])+ylim(-25,15)
   Plot_73

Plot Trust

   Plot_74 <- ggplot(data=p.dat_weekly.list[[13]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.28083))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.28083),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*-0.13294),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*-0.13294)),linetype=2)+ggtitle(label.list[13])+ylim(-25,15)
   Plot_74

Plot Risk Taking

   Plot_75 <- ggplot(data=p.dat_weekly.list[[14]],aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.06918))+geom_line(group=1,aes(x=c("2 weeks","3 weeks","4 weeks","5 weeks"),y=mean.1+mean.2*0.06918),linetype=2)+geom_point(size=4,shape=22,fill="red")+geom_hline(yintercept=0,linetype=2)+xlab("")+theme_bw()+ylab("Effect on New Mortality Growth (%)")+
   geom_point(size=4,aes(y=mean.1+mean.2*-0.15774),shape=21,fill="blue")+geom_line(group=1,aes(y=c(mean.1+mean.2*-0.15774)),linetype=2)+ggtitle(label.list[14])+ylim(-25,15)
   Plot_75

Arrange all plots

Plot_76 <- grid.arrange(Plot_61, Plot_62, Plot_63, Plot_64, Plot_65, Plot_66, Plot_68, Plot_69, Plot_70, Plot_71, Plot_72, ncol=4,nrow=3)

Plot_76

## TableGrob (3 x 4) "arrange": 11 grobs
##     z     cells    name           grob
## 1   1 (1-1,1-1) arrange gtable[layout]
## 2   2 (1-1,2-2) arrange gtable[layout]
## 3   3 (1-1,3-3) arrange gtable[layout]
## 4   4 (1-1,4-4) arrange gtable[layout]
## 5   5 (2-2,1-1) arrange gtable[layout]
## 6   6 (2-2,2-2) arrange gtable[layout]
## 7   7 (2-2,3-3) arrange gtable[layout]
## 8   8 (2-2,4-4) arrange gtable[layout]
## 9   9 (3-3,1-1) arrange gtable[layout]
## 10 10 (3-3,2-2) arrange gtable[layout]
## 11 11 (3-3,3-3) arrange gtable[layout]

CROSS-SECTIONAL REGRESSION

# Importing libraries.
library(lubridate)
library(zoo)
library(quantmod)
library(fBasics)
library(tseries)
library(sandwich)
library(lmtest)
library(lattice)
library(xtable)
library(vars)
library(plyr)
library(gridExtra)
library(corrplot)
library(ggplot2)
library(reshape2)
library(data.table)
library(rvest)
library(foreign)
library(dplyr)
library(plm)
library(stringr)
library(stargazer)
library(survival)
library(ggfortify)
library(plotly)
library(sf)
library(readr)
library(mapview)
library(ggplot2)
library(tidyverse)
# library(rlang)
library(reshape)
library(rgdal)
library(lubridate)
library(plotly)
library(patchwork)
library(ggforce)
library(gridExtra)
library(htmltools)
library(data.table)
library(webshot)
library(coronavirus)
library(runner)
library(zoo)
library(DataCombine)
library(fastDummies)
library(car)
library(heatmaply)
library(htmlwidgets)
library(summarytools)
library(glmnet)
library(caret)
library(mlbench)
library(psych)
library(plm)
library(lmtest)
library(quantmod)
library(leaflet)
library(corrplot)
library(fBasics)
library(stargazer)
library(tseries)
library(vars)
library(dplyr)
library(haven)

Cleaning

# Remove factor variables for government responses.
datweekly_cs <- Final_Data_Country

datweekly_cs<-datweekly_cs %>% dplyr::select(-contains("Flag"))
datweekly_cs<-datweekly_cs %>% dplyr::select(-contains("Lagged"))
datweekly_cs$latitude <- NULL
datweekly_cs$longitude <- NULL

# Merge global preference survey data to Final_Data_Country
gps.country <- read_dta("data/global_preference_survey_country.dta")
# Change country names for merge
gps.country[which(gps.country$country == "South Korea"),"country"] <- "Korea, South"
gps.country[which(gps.country$country == "Czech Republic"),"country"] <- "Czechia"
gps.country[which(gps.country$country == "United States"),"country"] <- "US"

gps.individual <- read_dta("data/global_preference_survey_individual.dta")
# Change country names for merge
gps.individual[which(gps.individual$country == "South Korea"),"country"] <- "Korea, South"
gps.individual[which(gps.individual$country == "Czech Republic"),"country"] <- "Czechia"
gps.individual[which(gps.individual$country == "United States"),"country"] <- "US"

gps.individual$age <- as.character(gps.individual$age)
gps.individual[which(gps.individual$age == "99 99+"), "age"] <- "99"
gps.individual[which(gps.individual$age == "100 (Refused"), "age"] <- NA
gps.individual$age <- as.numeric(gps.individual$age)
gps.individual.pop65 <- gps.individual[which(!is.na(gps.individual$age) & gps.individual$age > 65), ]
gps.individual.pop65 <- gps.individual.pop65[which(!is.na(gps.individual.pop65$trust)),]
gps.country.pop65 <- ddply(gps.individual.pop65, "country", function(X) data.frame(wm.trust = weighted.mean(X$trust, X$wgt)))

colnames(gps.country)[1] <- "COUNTRY"
datweekly_cs <- merge(datweekly_cs, gps.country, by=c("COUNTRY"), all.x=TRUE)
colnames(gps.country.pop65)[1] <- "COUNTRY"
datweekly_cs <- merge(datweekly_cs, gps.country.pop65, by=c("COUNTRY"), all.x=TRUE)

Rename variables and create some new variables

datweekly_cs<-pdata.frame(datweekly_cs, index = c("COUNTRY", "Date"))
datweekly_cs$Date<-as.Date(datweekly_cs$Date,"%Y-%m-%d")
#reorder
datweekly_cs<-datweekly_cs[order(datweekly_cs$Date),]
datweekly_cs<-datweekly_cs[order(datweekly_cs$COUNTRY),]

colnames(datweekly_cs)[colnames(datweekly_cs) == "Total_Cases_Country"] <- "Total_Case_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "Total_Deceased_Country"] <- "Total_Death_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "New_Confirmed_Country"] <- "New_Case_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "New_Total_Deceased_Country"] <- "New_Death_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "Total_Mortality_Rate_Per_Capita"] <- "Total_Mortality_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "New_Mortality_Rate_Per_Capita"] <- "New_Mortality_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "Total_Cases_Country_Per_Capita"] <- "Total_Case_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "rolling_average_confirmed"] <- "RollingAverage_New_Case_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "rolling_average_deceased"] <- "RollingAverage_New_Death_Country"
colnames(datweekly_cs)[colnames(datweekly_cs) == "total_rolling_average_mortality"] <- "RollingAverage_Total_Mortality_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "new_rolling_average_mortality"] <- "RollingAverage_New_Mortality_Rate"
colnames(datweekly_cs)[colnames(datweekly_cs) == "EUI_democracy"] <- "EIU_Democracy"

datweekly_cs$RollingAverage_Total_Mortality_Rate <- NULL
datweekly_cs$longitude <- NULL
datweekly_cs$latitude <- NULL

datweekly_cs$New_Case_Rate = datweekly_cs$New_Case_Country/datweekly_cs$Population

datweekly_cs <- datweekly_cs %>%
 dplyr::group_by(COUNTRY) %>%
 dplyr::mutate(RollingAverage_New_Case_Rate = frollmean(New_Case_Rate, 7,na.rm=TRUE))

datweekly_cs <- datweekly_cs %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(Total_Mortality_Rate_Growth = log(Total_Mortality_Rate) - Lag(log(Total_Mortality_Rate),7))

datweekly_cs <- datweekly_cs %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(Total_Case_Rate_Growth = log(Total_Case_Rate) - Lag(log(Total_Case_Rate),7))

datweekly_cs <- datweekly_cs %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(New_Case_Rate_Growth = log(RollingAverage_New_Case_Rate) - Lag(log(RollingAverage_New_Case_Rate),7))

datweekly_cs <- datweekly_cs %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(New_Mortality_Rate_Growth = log(RollingAverage_New_Mortality_Rate) - Lag(log(RollingAverage_New_Mortality_Rate),7))

datweekly_cs$respiratory_deaths_rate <- 1/3*(datweekly_cs$respiratory_deaths/datweekly_cs$Population)

Reorder columns

datweekly_cs <- datweekly_cs %>%
 dplyr::select(c("COUNTRY", "X1", "Date",
          "Total_Case_Country", "Total_Death_Country", "New_Case_Country", "New_Death_Country", "Population",
          "Total_Case_Rate", "Total_Mortality_Rate", "New_Case_Rate", "New_Mortality_Rate", 
          "RollingAverage_New_Case_Country", "RollingAverage_New_Death_Country", 
          "RollingAverage_New_Case_Rate", "RollingAverage_New_Mortality_Rate", 
          "Total_Case_Rate_Growth", "Total_Mortality_Rate_Growth", 
          "New_Case_Rate_Growth", "New_Mortality_Rate_Growth"), everything())

datweekly_cs$Total_Case_Rate_Growth[is.nan(datweekly_cs$Total_Case_Rate_Growth)] <- NA
datweekly_cs$Total_Mortality_Rate_Growth[is.nan(datweekly_cs$Total_Mortality_Rate_Growth)] <- NA

datweekly_cs$New_Case_Rate_Growth[is.nan(datweekly_cs$New_Case_Rate_Growth)] <- NA
datweekly_cs$New_Mortality_Rate_Growth[is.nan(datweekly_cs$New_Mortality_Rate_Growth)] <- NA

datweekly_cs$New_Mortality_Rate_Growth[is.infinite(-datweekly_cs$New_Mortality_Rate_Growth)] <- Inf

colnames(datweekly_cs)

##  [1] "COUNTRY"                              
##  [2] "X1"                                   
##  [3] "Date"                                 
##  [4] "Total_Case_Country"                   
##  [5] "Total_Death_Country"                  
##  [6] "New_Case_Country"                     
##  [7] "New_Death_Country"                    
##  [8] "Population"                           
##  [9] "Total_Case_Rate"                      
## [10] "Total_Mortality_Rate"                 
## [11] "New_Case_Rate"                        
## [12] "New_Mortality_Rate"                   
## [13] "RollingAverage_New_Case_Country"      
## [14] "RollingAverage_New_Death_Country"     
## [15] "RollingAverage_New_Case_Rate"         
## [16] "RollingAverage_New_Mortality_Rate"    
## [17] "Total_Case_Rate_Growth"               
## [18] "Total_Mortality_Rate_Growth"          
## [19] "New_Case_Rate_Growth"                 
## [20] "New_Mortality_Rate_Growth"            
## [21] "C1_School.closing"                    
## [22] "C2_Workplace.closing"                 
## [23] "C3_Cancel.public.events"              
## [24] "C4_Restrictions.on.gatherings"        
## [25] "C5_Close.public.transport"            
## [26] "C6_Stay.at.home.requirements"         
## [27] "C7_Restrictions.on.internal.movement" 
## [28] "C8_International.travel.controls"     
## [29] "E1_Income.support"                    
## [30] "E2_Debt.contract.relief"              
## [31] "E3_Fiscal.measures"                   
## [32] "H1_Public.information.campaigns"      
## [33] "H2_Testing.policy"                    
## [34] "H3_Contact.tracing"                   
## [35] "H4_Emergency.investment.in.healthcare"
## [36] "H5_Investment.in.vaccines"            
## [37] "M1_Wildcard"                          
## [38] "StringencyIndex"                      
## [39] "LegacyStringencyIndex"                
## [40] "Oxford_Cases"                         
## [41] "Oxford_Deaths"                        
## [42] "Latitude"                             
## [43] "Longitude"                            
## [44] "prop65"                               
## [45] "propurban"                            
## [46] "popdensity"                           
## [47] "driving"                              
## [48] "walking"                              
## [49] "transit"                              
## [50] "arrivals"                             
## [51] "departures"                           
## [52] "vul_emp"                              
## [53] "GNI"                                  
## [54] "health_exp"                           
## [55] "pollution"                            
## [56] "EIU_Democracy"                        
## [57] "freedom_house"                        
## [58] "cellular_sub"                         
## [59] "milex"                                
## [60] "milper"                               
## [61] "irst"                                 
## [62] "pec"                                  
## [63] "cinc"                                 
## [64] "endocrine_deaths"                     
## [65] "blood_pressure_deaths"                
## [66] "respiratory_deaths"                   
## [67] "govt_accountability"                  
## [68] "political_stability"                  
## [69] "govt_effectiveness"                   
## [70] "regulatory_quality"                   
## [71] "rule_of_law"                          
## [72] "control_of_corruption"                
## [73] "isocode"                              
## [74] "patience"                             
## [75] "risktaking"                           
## [76] "posrecip"                             
## [77] "negrecip"                             
## [78] "altruism"                             
## [79] "trust"                                
## [80] "wm.trust"                             
## [81] "respiratory_deaths_rate"

Generate key endogenous variables used in cross-country analysis

country <- c("")

# Mortality
date.first.death <- c(0)
date.peak.mortality <- c(0)
days.to.peak.mortality <- c(0)
peak.or.no.mortality <- c(0)
peak.cum.mortality <- c(0)
peak.new.mortality <- c(0)
early.mortality <- c(0)
early.mortality.growth<-c(0)

# Case
date.first.case <- c(0)
date.peak.case <- c(0)
days.to.peak.case <- c(0)
peak.or.no.case <- c(0)
peak.cum.case <- c(0)
peak.new.case <- c(0)
early.case <- c(0)
early.case.growth <- c(0)

# Stringency Index
early.stringency.sum <- c(0)
peak.stringency <- c(0)
first.stringency <- c(0)
date.first.stringency <- c(0)
date.peak.stringency <- c(0)
early.stringency.average <- c(0)

# Mobility
early.mobility.walking<-c(0)

dems_data<-aggregate(data=datweekly_cs,cbind(prop65,propurban,popdensity,vul_emp,health_exp,GNI,pollution,Latitude,Longitude,EIU_Democracy)~COUNTRY,FUN=mean,na.rm=T)

for(i in 1:length(unique(dems_data$COUNTRY))){
  
  datweekly_csrevised<-subset(datweekly_cs,COUNTRY==unique(dems_data$COUNTRY)[i])
  country[i] <- as.character(unique(dems_data$COUNTRY)[i])
  datweekly_csrevised<-datweekly_csrevised[order(datweekly_csrevised$Date),]
  datweekly_csrevised<-datweekly_csrevised[order(datweekly_csrevised$COUNTRY),]
  datweekly_csrevised <- datweekly_csrevised %>%
    dplyr::group_by(COUNTRY) %>%
    dplyr::mutate(RollingAverage_Walking = frollmean(walking, 7))
  datweekly_csrevised <- datweekly_csrevised %>%
    dplyr::group_by(COUNTRY) %>%
    dplyr::mutate(RollingAverage_StringencyIndex = frollmean(StringencyIndex, 7))
  
  # Mortality
  date.first.death[i]<-as.character(datweekly_csrevised$Date[which(datweekly_csrevised$Total_Death_Country>0)[1]]) # Date of first death
  peak.new.mortality[i]<-max(datweekly_csrevised$RollingAverage_New_Mortality_Rate,na.rm = TRUE) # Peak new mortality
  date.peak.mortality[i]<-as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate==peak.new.mortality[i]))]) # Date of peak new mortality
  days.to.peak.mortality[i]<-as.Date(date.peak.mortality[i])-as.Date(date.first.death[i]) # Days from first death to peak new mortality
  peak.cum.mortality[i]<-datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate==peak.new.mortality[i])] # Total mortality rate at the peak
  peak.or.no.mortality[i]<-ifelse(date.peak.mortality[i]==max(datweekly_csrevised$Date),0,1)
  early.mortality[i]<-datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$Total_Death_Country>0)[7]] # Total mortality in the first week since first death
  early.mortality.growth[i]<-log(datweekly_csrevised$RollingAverage_New_Mortality_Rate[which(datweekly_csrevised$Total_Death_Country>0)[7]]) -
    log(datweekly_csrevised$RollingAverage_New_Mortality_Rate[which(datweekly_csrevised$Total_Death_Country>0)[1]])# Growth rate of new mortality rate in the first week since first death
  
  # Case
  date.first.case[i]<-as.character(datweekly_csrevised$Date[which(datweekly_csrevised$Total_Case_Country>0)[1]]) # Date of first case
  peak.new.case[i]<-max(datweekly_csrevised$RollingAverage_New_Case_Rate, na.rm = TRUE) # Peak new case
  date.peak.case[i]<-as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Case_Rate==peak.new.case[i]))]) # Date of peak new case
  days.to.peak.case[i]<-as.Date(date.peak.case[i])-as.Date(date.first.case[i]) # Days from first case to peak new case
  peak.cum.case[i]<-datweekly_csrevised$Total_Case_Rate[which.max(datweekly_csrevised$RollingAverage_New_Case_Rate==peak.new.case[i])] # Toal case rate at the peak
  peak.or.no.case[i]<-ifelse(date.peak.case[i]==max(datweekly_csrevised$Date),0,1)
  early.case[i]<-datweekly_csrevised$Total_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[7]] # Total case rate in the first week since first case
  if (datweekly_csrevised$RollingAverage_New_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[1]] == 0){
    early.case.growth[i]<-log(datweekly_csrevised$RollingAverage_New_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[7]]) -  log(datweekly_csrevised$Total_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[1]])# Growth rate of new case rate in the first week since first case
  }else{
    early.case.growth[i]<-log(datweekly_csrevised$RollingAverage_New_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[7]]) -  log(datweekly_csrevised$RollingAverage_New_Case_Rate[which(datweekly_csrevised$Total_Case_Country>0)[1]])# Growth rate of new case rate in the first week since first case
  }
  
  # Stringency Index
  early.stringency.sum[i] <- sum(datweekly_csrevised$StringencyIndex[which(datweekly_csrevised$Date<date.first.death[i] & !is.na(datweekly_csrevised$StringencyIndex))])  # Sum of stringency index prior to first death
  early.stringency.average[i] <- mean(datweekly_csrevised$StringencyIndex[which(datweekly_csrevised$Date<date.first.death[i] & !is.na(datweekly_csrevised$StringencyIndex))])  # Average of stringency index prior to first death
  # early.stringency[i]<-b$RollingAverage_StringencyIndex[which(b$Total_Death_Country>0)[1]-7] #Weekly average stringency index in the week prior to first death
  # early.stringency[i]<-b$StringencyIndex[which(b$Total_Death_Country>0)[1]-10] #Weekly average stringency index in the week prior to first death
  
  first.stringency[i] <- datweekly_csrevised$StringencyIndex[which(datweekly_csrevised$StringencyIndex > 0)[1]] # First SI, no matter before or after the first case
  # first.stringency[i] <- b$StringencyIndex[which(b$C1_School.closing > 0 | b$C2_Workplace.closing > 0 |
  #                                                b$C3_Cancel.public.events > 0 | b$C4_Restrictions.on.gatherings > 0 |
  #                                                b$C5_Close.public.transport > 0 | b$C6_Stay.at.home.requirements > 0 | 
  #                                                b$C7_Restrictions.on.internal.movement > 0)[1]] # First SI, no matter before or after the first case or death
  peak.stringency[i] <- max(datweekly_csrevised$StringencyIndex,na.rm=T) # Peak SI
  
  date.first.stringency[i] <- as.character(datweekly_csrevised$Date[which(datweekly_csrevised$StringencyIndex > 0)[1]]) # Date of first SI
  # date.first.stringency[i] <- as.character(b$Date[which(b$C1_School.closing > 0 | b$C2_Workplace.closing > 0 |
  #                                                b$C3_Cancel.public.events > 0 | b$C4_Restrictions.on.gatherings > 0 |
  #                                                b$C5_Close.public.transport > 0 | b$C6_Stay.at.home.requirements > 0 | 
  #                                                b$C7_Restrictions.on.internal.movement > 0)[1]]) # Date of first SI
  if (peak.stringency[i] == -Inf){
    date.peak.stringency[i] <- -Inf
  }else{
    date.peak.stringency[i] <- as.character(datweekly_csrevised$Date[which.max(datweekly_csrevised$StringencyIndex)])
  }
  
  # Mobility
  early.mobility.walking[i]<-datweekly_csrevised$RollingAverage_Walking[which(datweekly_csrevised$Total_Death_Country>0)[1]-1] # Weekly average walking mobility in the week prior to first death
  
}

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in max(datweekly_csrevised$StringencyIndex, na.rm = T): no non-missing
## arguments to max; returning -Inf

## Warning in max(datweekly_csrevised$StringencyIndex, na.rm = T): no non-missing
## arguments to max; returning -Inf

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in max(datweekly_csrevised$StringencyIndex, na.rm = T): no non-missing
## arguments to max; returning -Inf

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.mortality[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in peak.cum.mortality[i] <-
## datweekly_csrevised$Total_Mortality_Rate[which(datweekly_csrevised$RollingAverage_New_Mortality_Rate
## == : number of items to replace is not a multiple of replacement length

## Warning in date.peak.case[i] <-
## as.character(datweekly_csrevised$Date[which((datweekly_csrevised$RollingAverage_New_Case_Rate
## == : number of items to replace is not a multiple of replacement length

stringency.delta <- (peak.stringency-first.stringency)/as.numeric(as.Date(date.peak.stringency)-as.Date(date.first.stringency))
log.early.stringency.sum <- log(early.stringency.sum)
log.early.stringency.sum[which(is.infinite(-log.early.stringency.sum))] <- NA
how.early.stringency <- as.numeric(as.Date(date.first.death)-as.Date(date.first.stringency))
# early.stringency.average <- early.stringency/how.early.stringency
log.peak.mortality.to.duration <- log(peak.new.mortality)/as.numeric(days.to.peak.mortality)
log.peak.case.to.duration <- log(peak.new.case)/as.numeric(days.to.peak.case)

# set NA early stringency to 0
# early.string[is.na(early.string)]<-0

Set up cross-section data

surv.dat_weekly<-data.frame(country,days.to.peak.mortality,peak.or.no.mortality, days.to.peak.case,peak.or.no.case, early.mortality,early.mortality.growth, early.case,early.case.growth, peak.cum.mortality,peak.cum.case,peak.new.mortality,peak.new.case, log.peak.mortality.to.duration,log.peak.case.to.duration, early.stringency.average,early.stringency.sum,log.early.stringency.sum,how.early.stringency,stringency.delta,peak.stringency, early.mobility.walking,dems_data[,-1])

#remove vietnam
surv.dat_weekly<-surv.dat_weekly[-nrow(surv.dat_weekly),]
surv.dat_weekly<-mutate(surv.dat_weekly,stringent=ifelse(early.stringency.average>7.77,"SI>7.77","SI<7.77"),stringent=factor(stringent))

Cross-country analysis on the peak mortality

### - peak new mortality - 
cs.new.mortality <- lm(log(peak.new.mortality) ~ 
                       log(early.mortality)+early.mortality.growth+
                       early.stringency.average+how.early.stringency+
                       stringency.delta+early.mobility.walking+
                       prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly,na.action = "na.exclude")
se.new.mortality<-coeftest(cs.new.mortality, vcov = vcovHC(cs.new.mortality, type = "HC1"))

surv.dat_weekly$res.new.mortality = resid(cs.new.mortality)

### - peak new mortality to duration ratio - 
cs.peak.duration.ratio<- lm(log.peak.mortality.to.duration ~ 
                                  log(early.mortality)+early.mortality.growth+
                                  early.stringency.average+how.early.stringency+
                                  stringency.delta+early.mobility.walking+
                                  prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly,na.action = "na.exclude")
se.peak.duration.ratio<-coeftest(cs.peak.duration.ratio, vcov = vcovHC(cs.peak.duration.ratio, type = "HC1"))

surv.dat_weekly$res.peak.duration.ratio = resid(cs.peak.duration.ratio)

### - survival analysis on pandemic duration to first peak
cox.death<-coxph(Surv(days.to.peak.mortality,peak.or.no.mortality) ~
                   log(peak.new.mortality)+log(early.mortality)+early.mortality.growth+
                   early.stringency.average+how.early.stringency+
                   stringency.delta+early.mobility.walking+
                   prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly)

test.death<-cox.zph(cox.death)$table[nrow(cox.zph(cox.death)$table),ncol(cox.zph(cox.death)$table)]

HTML output of the cross-country analysis

stargazer(cs.new.mortality,cs.peak.duration.ratio,cox.death,
          se=list(se.new.mortality[,2],se.peak.duration.ratio[,2]),type="html",out=("Table_cs_reg_output_avgSI.htm"),intercept.bottom = FALSE,
          dep.var.labels=c("Log(Peak New Mortality Rate)","Log(Peak New Mortality Rate)-to-PD Ratio","Survival Probability of Mortality Peaking at Time (t)"),
          covariate.labels=c("Intercept","Log(Peak Mortality)","Log(Early Mortality)","Early Mortality Growth",
                             "Early SI","Days between First SI and First Death",
                             "Stringency Delta","Early Mobility",
                             "Prop. 65+","Prop. Urban","Pop. Density","Vulnerable Emp.", "Log(GNI)",
                             "EIU Democracy","Latitude:Longitude"), 
          df = FALSE,omit.stat =c("max.rsq","logrank"),
          notes = c("*,**,*** correspond to 10%, 5% and 1% significance, respectively.",
                    "PH Test refers to testing the proportional hazards assumption (Grambsch and Therneau (1994)).",
                    "Null hypothesis is the assumption is not violated.",
                    "Standard errors in linear models are Heteroscedastic-Robust standard errors."),
          notes.append=F,notes.align ="l",font.size = "tiny",
          title=("Cross-Country Regression: Explaining Differences in the Empirical Shape of First-Wave Mortality Rates"),
          add.lines = list(c("PH Test p-value",NA,NA,round(test.death,3))),
          table.layout = "-ldm#-t=sa-n")

Plot K-M curves for mortality (Figure 7).

km_fit <- survfit(Surv(days.to.peak.mortality,peak.or.no.mortality) ~1, data=surv.dat_weekly)

p1<-autoplot(km_fit)+theme_bw()+xlab("Number of Days")+ylab("Probability that Mortality Peak is Yet to Come")
km_fit2<-survfit(Surv(days.to.peak.mortality,peak.or.no.mortality)~stringent, data=surv.dat_weekly)
p2<-autoplot(km_fit2)+theme_bw()+xlab("Number of Days")+ylab("Probability that Mortality Peak is Yet to Come")+theme(legend.position = c(0.2, 0.2),legend.title=element_blank())
p3<-ggplot(data=surv.dat_weekly,aes(x=early.stringency.average,fill=stringent))+geom_histogram(color="black",alpha=.5)+theme_bw()+xlab("Average SI from the First Non-Zero SI to the First Death")+ylab("Frequency of Countries")+theme(legend.title = element_blank(),legend.position = c(0.7,0.6))

Plot_76 <- grid.arrange(p1, p2, p3, nrow=1)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 3 rows containing non-finite values (stat_bin).

# g <- arrangeGrob(p1,p2,p3,nrow=1)

Correlation matrix of explanatory variables (Table 3A).

cor.surv.dat <- surv.dat_weekly[,c("days.to.peak.mortality","early.mortality","early.mortality.growth",
                            "peak.new.mortality","log.peak.mortality.to.duration",
                            "early.stringency.average","how.early.stringency","stringency.delta",
                            "early.mobility.walking",
                            "prop65","propurban","popdensity","vul_emp","health_exp","GNI","pollution","Latitude","Longitude","EIU_Democracy")]

colnames(cor.surv.dat)<-c("PD to Peak Mortality", "Early Mortality", "Early Mortality Growth",
                     "Peak New Mortality", "Logged Peak Mortality-to-PD",
                     "Early SI","Days from First SI to First Death", "Stringency Delta",
                     "Early Mobility",
                     "Prop. 65+", "Prop. Urban", "Pop. Density", 
                     "Vulnerable Emp.","Health Exp.","GNI","Pollution","Latitude","Longitude","Democracy")
cormat<-round(cor(cor.surv.dat,use = "complete.obs"), 2)

# Here, I get the lower triangle of the correlation matrix
get_lower_tri<-function(cormat){
  cormat[upper.tri(cormat)] <- NA
  return(cormat)
}
# Now, I get the upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
  cormat[lower.tri(cormat)]<- NA
  return(cormat)
}
reorder_cormat <- function(cormat){
# Use correlation between variables as distance
dd <- as.dist((1-cormat)/2)
hc <- hclust(dd)
cormat <-cormat[hc$order, hc$order]
}
cormat <- reorder_cormat(cormat)
melted_cormat <- reshape2::melt(get_upper_tri(cormat), na.rm = TRUE)

### Generate the Static Correlation Map
ggheatmap <- ggplot(data = melted_cormat, aes(x=Var2, y=Var1, fill=value)) + 
  geom_tile(color = "black") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white",
    midpoint = 0, limit = c(-1,1), space = "Lab", 
    name="Pearson\nCorrelation") +
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45)) +
  theme(axis.text.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0))) +
  theme(axis.text.x = element_text(hjust=1))+coord_fixed()

Plot_77 <- ggheatmap + 
geom_text(aes(Var2, Var1, label = value), color = "black", size = 1.75) +
theme(
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  axis.text=element_text(size=7),
  panel.grid.major = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  axis.ticks = element_blank(),
  legend.justification = c(1, 0),
  legend.position = c(0.4, 0.8),
  legend.direction = "horizontal",
  legend.title = element_text(size = 6),
  legend.text=element_text(size=6))+
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                title.position = "top", title.hjust = 0.5))

# Print the heatmap
print(Plot_77)

Correlation matrix of government interventions (Table 3B)

cor.policy.dat <- datweekly_cs[,c("C1_School.closing", 
                      "C2_Workplace.closing",
                      "C3_Cancel.public.events",
                      "C4_Restrictions.on.gatherings",
                      "C5_Close.public.transport",            
                      "C6_Stay.at.home.requirements",         
                      "C7_Restrictions.on.internal.movement", 
                      "C8_International.travel.controls",
                      "H1_Public.information.campaigns",
                      "StringencyIndex")]
colnames(cor.policy.dat) <- c("School Closing", 
                      "Workplace Closing",
                      "Cancel Public Events",
                      "Gathering Restrictions",
                      "Close Public Transport",            
                      "Stay at Home",         
                      "Internal Movement Restrictions", 
                      "International Travel Controls",
                      "Public Information Campaigns",
                      "Stringency Index")
cormat<-round(cor(cor.policy.dat,use = "complete.obs"), 2)

# Here, I get the lower triangle of the correlation matrix
get_lower_tri<-function(cormat){
  cormat[upper.tri(cormat)] <- NA
  return(cormat)
}
# Now, I get the upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
  cormat[lower.tri(cormat)]<- NA
  return(cormat)
}
reorder_cormat <- function(cormat){
# Use correlation between variables as distance
dd <- as.dist((1-cormat)/2)
hc <- hclust(dd)
cormat <-cormat[hc$order, hc$order]
}
cormat <- reorder_cormat(cormat)
melted_cormat <- reshape2::melt(get_upper_tri(cormat), na.rm = TRUE)

### Generate the Static Correlation Map
ggheatmap <- ggplot(data = melted_cormat, aes(x=Var2, y=Var1, fill=value)) + 
  geom_tile(color = "black") +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white",
    midpoint = 0, limit = c(-1,1), space = "Lab", 
    name="Pearson\nCorrelation") +
  theme_minimal()+
  theme(axis.text.x = element_text(angle = 45)) +
  theme(axis.text.x = element_text(margin = margin(t = 5, r = 0, b = 0, l = 0))) +
  theme(axis.text.x = element_text(hjust=1))+coord_fixed()

Plot_78 <- ggheatmap + 
geom_text(aes(Var2, Var1, label = value), color = "black", size = 1.75) +
theme(
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  axis.text=element_text(size=7),
  panel.grid.major = element_blank(),
  panel.border = element_blank(),
  panel.background = element_blank(),
  axis.ticks = element_blank(),
  legend.justification = c(1, 0),
  legend.position = c(0.6, 0.7),
  legend.direction = "horizontal",
  legend.title = element_text(size = 7),
  legend.text=element_text(size=7),
  plot.caption = element_text(size = 7, hjust=0))+
  guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
                title.position = "top", title.hjust = 0.5))

# Print the heatmap
print(Plot_78)

Summary statistics of country characteristics (Table 3C)

cor.sum.dat <- surv.dat_weekly[,c("days.to.peak.mortality","early.mortality","early.mortality.growth",
                            "peak.new.mortality","log.peak.mortality.to.duration",
                            "early.stringency.average","how.early.stringency","stringency.delta",
                            "early.mobility.walking",
                            "prop65","propurban","popdensity","vul_emp","health_exp","GNI","pollution","Latitude","Longitude","EIU_Democracy")]
cor.sum.dat$early.mortality <- cor.sum.dat$early.mortality * 10000000
cor.sum.dat$peak.new.mortality <- cor.sum.dat$peak.new.mortality * 10000000
cor.sum.dat$GNI <- log(cor.sum.dat$GNI)
colnames(cor.sum.dat)<-c("PD to Peak Mortality", "Early Mortality", "Early Mortality Growth",
                     "Peak New Mortality", 
                     "Logged Peak Mortality-to-PD",
                     "Early SI","Days from First SI to First Death", "Stringency Delta",
                     "Early Mobility",
                     "Prop. 65+", "Prop. Urban", "Pop. Density", 
                     "Vulnerable Employment","Health Expenditure","Log(GNI)","Pollution","Latitude","Longitude","Democracy")
sum.stats <- t(round(basicStats(cor.sum.dat),3))
sum.stats <- data.frame(sum.stats[,c("nobs", "NAs", "Minimum", "Maximum", "1. Quartile", "3. Quartile", "Mean", "Median", "Stdev")])
sum.stats$N <- sum.stats$nobs - sum.stats$NAs
sum.stats <- sum.stats[,-c(1,2)]
sum.stats <- sum.stats %>%
  dplyr::select(c("N", "Minimum", "Maximum", "Mean", "Median", "Stdev"), everything())
colnames(sum.stats) <- c("N", "Minimum", "Maximum", "Mean", "Median", "S.D.", "25 Percentile", "75 Percentile")
peak.stringency.finite <- cor.sum.dat$`Peak Stringency`[which(is.finite(cor.sum.dat$`Peak Stringency`))]
stargazer(sum.stats,type="html",summary = FALSE, out=("Table_summary_country_char.htm"),title="Country Characteristics: Summary")

## 
## <table style="text-align:center"><caption><strong>Country Characteristics: Summary</strong></caption>
## <tr><td colspan="9" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>N</td><td>Minimum</td><td>Maximum</td><td>Mean</td><td>Median</td><td>S.D.</td><td>25 Percentile</td><td>75 Percentile</td></tr>
## <tr><td colspan="9" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">PD.to.Peak.Mortality</td><td>58</td><td>3</td><td>85</td><td>33.983</td><td>33.500</td><td>15.550</td><td>26</td><td>40</td></tr>
## <tr><td style="text-align:left">Early.Mortality</td><td>58</td><td>0.022</td><td>67.944</td><td>8.055</td><td>2.865</td><td>13.840</td><td>0.863</td><td>8.204</td></tr>
## <tr><td style="text-align:left">Early.Mortality.Growth</td><td>58</td><td>0</td><td>3.611</td><td>1.369</td><td>1.386</td><td>0.990</td><td>0.693</td><td>1.946</td></tr>
## <tr><td style="text-align:left">Peak.New.Mortality</td><td>58</td><td>0.368</td><td>287.696</td><td>32.445</td><td>8.339</td><td>55.498</td><td>2.032</td><td>29.007</td></tr>
## <tr><td style="text-align:left">Logged.Peak.Mortality.to.PD</td><td>58</td><td>-5.492</td><td>-0.187</td><td>-0.655</td><td>-0.417</td><td>0.907</td><td>-0.514</td><td>-0.322</td></tr>
## <tr><td style="text-align:left">Early.SI</td><td>55</td><td>0</td><td>27.450</td><td>7.773</td><td>6.309</td><td>5.891</td><td>3.633</td><td>10.192</td></tr>
## <tr><td style="text-align:left">Days.from.First.SI.to.First.Death</td><td>55</td><td>-8</td><td>79</td><td>32.855</td><td>32</td><td>22.047</td><td>15.500</td><td>51</td></tr>
## <tr><td style="text-align:left">Stringency.Delta</td><td>55</td><td>0.479</td><td>5.930</td><td>1.713</td><td>1.301</td><td>1.181</td><td>0.969</td><td>1.796</td></tr>
## <tr><td style="text-align:left">Early.Mobility</td><td>51</td><td>26.130</td><td>149.684</td><td>92.657</td><td>96.114</td><td>31.365</td><td>65.111</td><td>111.594</td></tr>
## <tr><td style="text-align:left">Prop..65.</td><td>58</td><td>1.085</td><td>27.576</td><td>14.004</td><td>15.212</td><td>6.442</td><td>8.479</td><td>19.410</td></tr>
## <tr><td style="text-align:left">Prop..Urban</td><td>58</td><td>34.030</td><td>100</td><td>76.513</td><td>80.239</td><td>15.141</td><td>67.027</td><td>87.216</td></tr>
## <tr><td style="text-align:left">Pop..Density</td><td>58</td><td>3.249</td><td>7,952.998</td><td>295.055</td><td>107.557</td><td>1,047.129</td><td>32.179</td><td>213.004</td></tr>
## <tr><td style="text-align:left">Vulnerable.Employment</td><td>58</td><td>0.144</td><td>74.270</td><td>16.513</td><td>10.662</td><td>15.229</td><td>7.396</td><td>21.522</td></tr>
## <tr><td style="text-align:left">Health.Expenditure</td><td>58</td><td>69.293</td><td>10,246.140</td><td>2,615.687</td><td>1,589.132</td><td>2,453.654</td><td>666.110</td><td>4,336.231</td></tr>
## <tr><td style="text-align:left">Log.GNI.</td><td>58</td><td>7.611</td><td>11.343</td><td>9.965</td><td>10.147</td><td>0.969</td><td>9.256</td><td>10.753</td></tr>
## <tr><td style="text-align:left">Pollution</td><td>58</td><td>5.861</td><td>91.187</td><td>22.256</td><td>16.030</td><td>21.446</td><td>10.392</td><td>21.295</td></tr>
## <tr><td style="text-align:left">Latitude</td><td>58</td><td>-40.901</td><td>64.963</td><td>31.292</td><td>38.027</td><td>27.425</td><td>23.477</td><td>51</td></tr>
## <tr><td style="text-align:left">Longitude</td><td>58</td><td>-106.347</td><td>174.886</td><td>24.668</td><td>19.324</td><td>61.076</td><td>2.778</td><td>46.881</td></tr>
## <tr><td style="text-align:left">Democracy</td><td>58</td><td>1.930</td><td>9.870</td><td>7.088</td><td>7.510</td><td>1.991</td><td>6.605</td><td>8.262</td></tr>
## <tr><td colspan="9" style="border-bottom: 1px solid black"></td></tr></table>

Visualization of mortality curves

datweekly_cs <- datweekly_cs %>%
  dplyr::group_by(COUNTRY) %>%
  dplyr::mutate(First_Date_Mortality = as.character(Date[which(Total_Death_Country>0)[1]]))

datweekly_cs$Days_Since_First_Death <- as.Date(as.character(datweekly_cs$Date))-as.Date(as.character(datweekly_cs$First_Date_Mortality))

datweekly_csnormalized <- datweekly_cs[which(datweekly_cs$First_Date_Mortality>-1),]

Plot_79 <- plot_ly(datweekly_csnormalized, x=~Days_Since_First_Death, y=~RollingAverage_New_Mortality_Rate) %>%
  add_lines(linetype = ~COUNTRY) %>%
  layout(xaxis = list(range = c(0, 50)))
Plot_79

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Outcome variables (Generating Figure 3).

datweekly_csnormalized_v0 <- datweekly_csnormalized[which(datweekly_cs$COUNTRY == "Czechia"),]

Mortality_curve_v0 <- ggplot(data=datweekly_csnormalized_v0, aes(x=Days_Since_First_Death, y=RollingAverage_New_Mortality_Rate)) +
  geom_line(color="blue", size = 0.75) + geom_segment(aes(x=23, xend=23, y=0, yend=max(datweekly_csnormalized_v0$RollingAverage_New_Mortality_Rate[which(!is.na(datweekly_csnormalized_v0$RollingAverage_New_Mortality_Rate))])), size=1, colour="blue", linetype="dotted") + geom_ribbon(data = datweekly_csnormalized_v0 %>% dplyr::filter(Days_Since_First_Death < 24), aes(ymin = 0, ymax = datweekly_csnormalized_v0$RollingAverage_New_Mortality_Rate[which(datweekly_csnormalized_v0$Days_Since_First_Death<24)]), fill = "red", alpha = .5) + xlim(0, 50) + labs(x = "Days since the first death", y = "Daily new mortality rate, 7-day rolling average") + theme_bw() + theme(axis.text.x = element_text(size = 10), axis.text.y = element_text(size = 10), axis.title = element_text(size = 14))
Mortality_curve_v0

## Warning: Removed 82 row(s) containing missing values (geom_path).

Same peak, different durations: Norway versus Hungary

datweekly_csnormalized_v1 <- datweekly_csnormalized[which(datweekly_cs$COUNTRY == "Hungary" | datweekly_cs$COUNTRY == "Norway"),]


Mortality_curve_v1 <- ggplot(data=datweekly_csnormalized_v1, aes(x=Days_Since_First_Death, y=RollingAverage_New_Mortality_Rate, group=COUNTRY)) +
  geom_line(aes(color=COUNTRY), size = 0.75) + xlim(0, 50) + labs(x = "Days since the first death", y = "Daily new mortality rate, 7-day rolling average") + theme_bw() + theme(legend.position = c(0.2, 0.8), legend.title = element_blank(), legend.text=element_text(size=8))

Mortality_curve_v1

## Warning: Removed 149 row(s) containing missing values (geom_path).

Same duration, different peaks: Norway versus Denmark

datweekly_csnormalized_v2 <- datweekly_csnormalized[which(datweekly_cs$COUNTRY == "Denmark" | datweekly_cs$COUNTRY == "Norway"),]


Mortality_curve_v2 <- ggplot(data=datweekly_csnormalized_v2, aes(x=Days_Since_First_Death, y=RollingAverage_New_Mortality_Rate, group=COUNTRY)) +
  geom_line(aes(color=COUNTRY), size = 0.75) + xlim(0, 50) + labs(x = "Days since the first death", y = "Daily new mortality rate, 7-day rolling average") + theme_bw() + theme(legend.position = c(0.2, 0.8), legend.title = element_blank(), legend.text=element_text(size=8))

Mortality_curve_v2

## Warning: Removed 148 row(s) containing missing values (geom_path).

Different durations, different peaks, different ratios: Austria, Czechia versus Estonia

datweekly_csnormalized_v3 <- datweekly_csnormalized[which(datweekly_cs$COUNTRY == "Austria" | datweekly_cs$COUNTRY == "Estonia" | datweekly_cs$COUNTRY == "Greece"),]


Mortality_curve_v3 <- ggplot(data=datweekly_csnormalized_v3, aes(x=Days_Since_First_Death, y=RollingAverage_New_Mortality_Rate, group=COUNTRY)) +
  geom_line(aes(color=COUNTRY), size = 0.75) + xlim(0, 50) + labs(x = "Days since the first death", y = "Daily new mortality rate, 7-day rolling average") + theme_bw() + theme(legend.position = c(0.15, 0.85),legend.title = element_blank(), legend.text=element_text(size=8))

Mortality_curve_v3

## Warning: Removed 228 row(s) containing missing values (geom_path).

Generating Figure 4

Plot_80 <- grid.arrange(Mortality_curve_v1, Mortality_curve_v2, Mortality_curve_v3, nrow=1)

## Warning: Removed 149 row(s) containing missing values (geom_path).

## Warning: Removed 148 row(s) containing missing values (geom_path).

## Warning: Removed 228 row(s) containing missing values (geom_path).

Plot_80

## TableGrob (1 x 3) "arrange": 3 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (1-1,3-3) arrange gtable[layout]

Cross-country analysis on the peak mortality

### - peak new mortality - 
cs.new.mortality <- lm(log(peak.new.mortality) ~ 
                       log(early.mortality)+early.mortality.growth+
                       early.stringency.average+how.early.stringency+
                       stringency.delta+
                       prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly,na.action = "na.exclude")
se.new.mortality<-coeftest(cs.new.mortality, vcov = vcovHC(cs.new.mortality, type = "HC1"))

surv.dat_weekly$res.new.mortality = resid(cs.new.mortality)

### - peak new mortality to duration ratio - 
cs.peak.duration.ratio<- lm(log.peak.mortality.to.duration ~ 
                                  log(early.mortality)+early.mortality.growth+
                                  early.stringency.average+how.early.stringency+
                                  stringency.delta+
                                  prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly,na.action = "na.exclude")
se.peak.duration.ratio<-coeftest(cs.peak.duration.ratio, vcov = vcovHC(cs.peak.duration.ratio, type = "HC1"))

surv.dat_weekly$res.peak.duration.ratio = resid(cs.peak.duration.ratio)

### - survival analysis on pandemic duration to first peak
cox.death<-coxph(Surv(days.to.peak.mortality,peak.or.no.mortality) ~
                   log(peak.new.mortality)+log(early.mortality)+early.mortality.growth+
                   early.stringency.average+how.early.stringency+
                   stringency.delta+
                   prop65+propurban+popdensity+vul_emp+log(GNI)+EIU_Democracy+Latitude:Longitude,data=surv.dat_weekly)

test.death<-cox.zph(cox.death)$table[nrow(cox.zph(cox.death)$table),ncol(cox.zph(cox.death)$table)]

Residual map - peak new mortality (Figure A.2.1.)

require(maps)

## Loading required package: maps

## 
## Attaching package: 'maps'

## The following object is masked from 'package:plyr':
## 
##     ozone

## The following object is masked from 'package:purrr':
## 
##     map

require(viridis)
world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill="lightgray", colour = "white")

colnames(world_map)[1] <- "Longitude"
colnames(world_map)[2] <- "Latitude"

world_map$region[world_map$region == "Czech Republic"] <- "Czechia"
world_map$region[world_map$region == "USA"] <- "US"
world_map$region[world_map$region == "UK"] <- "United Kingdom"
world_map$region[world_map$region == "South Korea"] <- "Korea, South"

colnames(world_map)[5] <- "country"

surv.dat.map <- left_join(world_map, surv.dat_weekly, by = "country")

## Warning: Column `country` joining character vector and factor, coercing into
## character vector

colnames(surv.dat.map)[1] <- "Longitude"
colnames(surv.dat.map)[2] <- "Latitude"

# Finally, we can construct our initial marker map. First, I set different colors for each quantile.
surv.dat.map$abs.res.new.mortality <- abs(surv.dat.map$res.new.mortality)

p.res.new.mortality <- ggplot(surv.dat.map, aes(x = Longitude, y = Latitude)) +
  geom_polygon(aes(group = group, fill = round(res.new.mortality,2)), color = "white") +
  # geom_text(aes(label = round(cs.total.mortality.res,2)), data = surv.dat, size = 2)+
  # scale_fill_viridis(option="C", begin = min(surv.dat$cs.total.mortality.res[!is.na(surv.dat$cs.total.mortality.res)]), end = max(surv.dat$cs.total.mortality.res[!is.na(surv.dat$cs.total.mortality.res)]))+
  theme_void()+
  labs(fill = "Residuals")+
  theme(legend.text = element_text(size = 7),legend.title=element_text(size=8), legend.position = c(0.95,0.5))+
  scale_fill_gradient(low = "red", high = "blue", na.value = "lightgrey",guide = "colourbar")+
  guides(fill = guide_colourbar(barwidth = 1.1, barheight = 3.3))

p.res.new.mortality 

surv.dat.sorted.new.mortality <- surv.dat_weekly[order(-surv.dat_weekly$res.new.mortality),]

res.new.mortality.rank <- surv.dat.sorted.new.mortality[!is.na(surv.dat.sorted.new.mortality$res.new.mortality),c("country", "res.new.mortality")]
colnames(res.new.mortality.rank) <- c("Country", "Residual")
rownames(res.new.mortality.rank) <- NULL
under.predicted.new.mortality <- res.new.mortality.rank[c(1:5),]
over.predicted.new.mortality <- res.new.mortality.rank[c(seq(nrow(res.new.mortality.rank),nrow(res.new.mortality.rank)-4,-1)),]
stargazer(under.predicted.new.mortality,type="html",rownames = FALSE, summary = FALSE, out=("Table_res_new_mortality_under_predicted.htm"))

## 
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Country</td><td>Residual</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">France</td><td>2.715</td></tr>
## <tr><td style="text-align:left">Peru</td><td>2.207</td></tr>
## <tr><td style="text-align:left">Belgium</td><td>2.140</td></tr>
## <tr><td style="text-align:left">Kuwait</td><td>1.862</td></tr>
## <tr><td style="text-align:left">Ireland</td><td>1.589</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr></table>

stargazer(over.predicted.new.mortality,type="html",rownames = FALSE, summary = FALSE, out=("Table_res_new_mortality_over_predicted.htm"))

## 
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Country</td><td>Residual</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Australia</td><td>-2.343</td></tr>
## <tr><td style="text-align:left">Japan</td><td>-1.670</td></tr>
## <tr><td style="text-align:left">Thailand</td><td>-1.580</td></tr>
## <tr><td style="text-align:left">Korea, South</td><td>-1.542</td></tr>
## <tr><td style="text-align:left">China</td><td>-1.479</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr></table>

Residual map - peak new mortality-to-PD ratio (Figure A.2.2.)

require(maps)
require(viridis)
world_map <- map_data("world")
ggplot(world_map, aes(x = long, y = lat, group = group)) +
  geom_polygon(fill="lightgray", colour = "white")

colnames(world_map)[1] <- "Longitude"
colnames(world_map)[2] <- "Latitude"

world_map$region[world_map$region == "Czech Republic"] <- "Czechia"
world_map$region[world_map$region == "USA"] <- "US"
world_map$region[world_map$region == "UK"] <- "United Kingdom"
world_map$region[world_map$region == "South Korea"] <- "Korea, South"

colnames(world_map)[5] <- "country"

surv.dat.map <- left_join(world_map, surv.dat_weekly, by = "country")

## Warning: Column `country` joining character vector and factor, coercing into
## character vector

colnames(surv.dat.map)[1] <- "Longitude"
colnames(surv.dat.map)[2] <- "Latitude"

surv.dat.map$abs.res.peak.duration.ratio <- abs(surv.dat.map$res.peak.duration.ratio)

p.res.peak.duration.ratio <- ggplot(surv.dat.map, aes(x = Longitude, y = Latitude)) +
  geom_polygon(aes(group = group, fill = round(res.peak.duration.ratio,2)), color = "white") +
  # geom_text(aes(label = round(cs.total.mortality.res,2)), data = surv.dat, size = 2)+
  # scale_fill_viridis(option="C", begin = min(surv.dat$cs.total.mortality.res[!is.na(surv.dat$cs.total.mortality.res)]), end = max(surv.dat$cs.total.mortality.res[!is.na(surv.dat$cs.total.mortality.res)]))+
  theme_void()+
  labs(fill = "Residuals")+
  theme(legend.text = element_text(size = 7),legend.title=element_text(size=8), legend.position = c(0.95,0.5))+
  scale_fill_gradient(low = "green", high = "blue", na.value = "lightgrey",guide = "colourbar")+
  guides(fill = guide_colourbar(barwidth = 1.1, barheight = 3.3))

p.res.peak.duration.ratio

surv.dat.sorted.peak.to.PD.ratio <- surv.dat_weekly[order(-surv.dat_weekly$res.peak.duration.ratio),]

res.ratio.rank <- surv.dat.sorted.peak.to.PD.ratio[!is.na(surv.dat.sorted.peak.to.PD.ratio$res.peak.duration.ratio),c("country", "res.peak.duration.ratio")]
colnames(res.ratio.rank) <- c("Country", "Residual")
rownames(res.ratio.rank) <- NULL
under.predicted.ratio <- res.ratio.rank[c(1:5),]
over.predicted.ratio <- res.ratio.rank[c(seq(nrow(res.ratio.rank),nrow(res.ratio.rank)-4,-1)),]
stargazer(under.predicted.ratio,type="html",rownames = FALSE, summary = FALSE, out=("Table_res_ratio_under_predicted.htm"))

## 
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Country</td><td>Residual</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Egypt</td><td>0.682</td></tr>
## <tr><td style="text-align:left">Hungary</td><td>0.667</td></tr>
## <tr><td style="text-align:left">Kuwait</td><td>0.633</td></tr>
## <tr><td style="text-align:left">Peru</td><td>0.601</td></tr>
## <tr><td style="text-align:left">Belgium</td><td>0.546</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr></table>

stargazer(over.predicted.ratio,type="html",rownames = FALSE, summary = FALSE, out=("Table_res_ratio_over_predicted.htm"))

## 
## <table style="text-align:center"><tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Country</td><td>Residual</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Jordan</td><td>-3.256</td></tr>
## <tr><td style="text-align:left">Iceland</td><td>-1.176</td></tr>
## <tr><td style="text-align:left">India</td><td>-0.937</td></tr>
## <tr><td style="text-align:left">China</td><td>-0.474</td></tr>
## <tr><td style="text-align:left">Austria</td><td>-0.473</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr></table>